Financial Stability Review (FSR) - Bank · PDF fileThe preparation of the Financial Stability...

The preparation of the Financial Stability Review (FSR) is one of the avenues

through which Bank Indonesia achieves its mission “to safeguard the stability of the Indonesian

Rupiah by maintaining monetary and financial system stability for sustainable national

economic development”.

Publisher :

Bank Indonesia

Jl. MH Thamrin No.2, Jakarta

Indonesia

Information and Orders:

This edition is published in March 2011 and is based on data and information available as of December 2010, unless stated

otherwise.

The PDF format is downloadable from: http://www.bi.go.id

For inquiries, comments and feedback please contact:

Bank Indonesia

Directorate of Banking Research and Regulation

Financial System Stability Bureau

Jl.MH Thamrin No.2, Jakarta, Indonesia

Phone : (+62-21) 381 8902, 381 8075

Fax : (+62-21) 351 8629

Email : [email protected]

FSR is published biannually with the objectives:

To improve public insight in terms of understanding financial system stability.

To evaluate potential risks to financial system stability.

To analyze the developments of and issues within the financial system.

To offer policy recommendations to promote and maintain financial system stability.

Financial Stability Review

( No. 16, March 2011)

Bolstering Financial System Stability amid a deluge of Foreign Capital Inflows

Theme:

Directorate of Banking Research and Regulation

Financial System Stability Bureau

iii

Foreword .............. .................................................................................................................................................. vi

Overview ......... ........................................................................................................................................................ 1

Chapter 1. Risk from the Global Environment ............................................................................................................ 5

1.1. Sources of Vulnerability ........................................................................................................................... 8

1.2. Strengthening Risk in Indonesia............................................................................................................... 12

Chapter 2. Financial System Resilience ....................................................................................................................... 21

2.1. Financial System Structure and Resilience ................................................................................................. 23

2.2. Banking System Risk................................................................................................................................. 24

2.3. Potential Financial Market Risk and Financing........................................................................................... 36

Boks 2.1. Bank Liquidity Resilience ................................................................................................................. 46

Boks 2.2. Undisbursed Loans.......................................................................................................................... 49

Boks 2.3. Impact of Credit on Inflation ........................................................................................................... 52

Boks 2.4. Prime Lending Rate Policy Transparency .......................................................................................... 54

Boks 2.5. Sources of Bank Profitability ............................................................................................................ 56

Chapter 3. Strengthening Financial Infrastructure....................................................................................................... 59

3.1. Payment System Efficiency ....................................................................................................................... 61

3.2. Payment System Performance and Risk Mitigation.................................................................................... 65

Chapter 4. Special Topic ............................................................................................................................................ 67

4.1. Financial Sector Reform ........................................................................................................................... 69

4.2. Financial System Safety Net: Cooperation To Create And Maintain Financial System Stability.................... 76

4.3. Crisis Management Protocol (CMP) .......................................................................................................... 77

Boks 4.1 Financial Inclusion ............................................................................................................................. 79

Boks 4.2 Green Banking.................................................................................................................................. 82

Chapter 5. Financial System Stability Challenges and Prospects .................................................................................. 85

5.1. Future External and Internal Economic Conditions .................................................................................... 87

5.2. Financial System Stability Challenges ........................................................................................................ 88

5.3. Financial System Stability Outlook ............................................................................................................ 90

Article........................................................................................................................................................................ 91

Article 1 Risk Behaviour in the Monetary Policy Transmission Mechanism in Indonesia .................................. ... 93

Article 2 Towards Stronger ASEAN financial System : A Proposal for Regional Financial Stability Framework .... 107

Table of Contents

iv

List of Tables and Figures

Tables Figures

1.1 Breakdown of Global Economic Growth......... 81.2 State Budget Realisation for Semester I & II, 2010 151.3 Household Expenses and Income.................... 171.4 Effect of Rupiah Depreciation on

Conglomerate Equity ..................................... 19

2.1 Number of Financial Institutions ..................... 232.2 Deposit Withdrawals Simulation..................... 262.3 Profit/Loss ...................................................... 342.4 VaR SUN ........................................................ 382.5 SBN Ownership .............................................. 382.6 Indices of several Global Stock Markets .......... 402.7 Share Price Indices by Sector .......................... 402.8 Jakarta Composite Index (Commodities) ..... 402.9 Financial Ratios of Finance Companies ........ 442.10 NPL of Finance Companies.......................... 442.11 NPL performance of Finance Companies ..... 45

3.1 BI-SSSS Transactions ................................... 63

4.1 Salient Regulations and ImplementationSchedule of Basel III .................................... 72

4.2 Recapitulation of Capital Ratio andLeverage Ratio............................................ 73

5.1 Projections of GDP and Inflation ................. 875.2 Economic Growth in Indonesia according

to Type....................................................... 88

Tables Box

2.1.1 LCR by Bank Group .................................... 472.1.2 NSFR by Bank Group................................... 47

1.1 Global Stock Market Indices (2000=100)........ 91.2 CDS in GIIPS countries and Germany.............. 91.3 CDS in leading Eurozone countries and

Switzerland .................................................... 91.4 CDS in several Asian Countries....................... 101.5 Stock Market Indices in several Countries

in 2010.......................................................... 101.6 Private Capital Flows – FDI and Portfolio to

EM in Asia, Europe and Latin America ............ 101.7 Private Capital Flows to Emerging Markets ..... 111.8 Global Price Indices ........................................ 111.9 Price Indices of several International

Commodities ................................................. 111.10 UBS CMCI Composite Price Index by Sector.... 121.11 Indonesian Development of Non-Oil Export

Import............................................................ 121.12 Indonesia Development of Total Exports and

Imports .......................................................... 121.13 Rupiah Exchange Rate.................................... 131.14 Rupiah Exchange Rate Volatility...................... 131.15 Composition of Direct and Portfolio Investment

to Indonesia ................................................... 131.16 Inflation in Indonesia (Core and Headline) ...... 141.17 Price Indices of Foodstuffs and Oil in Indonesia,

2006 = 100.................................................... 141.18 Inflation in several Advanced Countries .......... 141.19 Inflation in several ASEAN Countries .............. 141.20 Real Interest Rates.......................................... 151.21 Consumer Confidence Index .......................... 161.22 Composition of Household Expenditure

2009-2010..................................................... 161.23 Price Expectation Index for 3 & 6 Months ....... 171.24 Credit and NPL in the Household Sector ......... 171.25 ROA& ROE of Non-financial Public Listed

Companies..................................................... 181.26 DER and TL/TAof Non-financial

Public Listed Companies................................. 181.27 Key Financial Indicators for the Corporate

Sector ............................................................ 181.28 Ratio of Net Foreign Liabilities to Equity.......... 19

v

Figures Box

2.1 Asset Compositions of Financial Institutions ... 232.2 Financial Stability Index, 1996-2010 ............... 242.3 Share of Bank Funding and Financing............. 242.4 Deposit Growths by Semester ........................ 242.5 Deposit Growth based on Ownership............. 252.6 Liquid Assets by Component .......................... 252.7 Share of Placements held at Bank Indonesia ... 252.8 Credit Growth by Type................................... 272.9 Property Credit Growth.................................. 272.10 Credit Growth by Currency ............................ 272.11 Loan to Deposit Ratio by Currency ................. 272.12 MSM Credit Growth (yoy) .............................. 282.13 MSM Lending Rates (%)................................. 282.14 Share of MSM Credit ..................................... 292.15 Loans to Deposits Ratio (LDR) ......................... 292.16 Non-Performing Loans (NPL)........................... 292.17 NPL Ratio by Loan Type .................................. 302.18 NPL Ratio by Currency.................................... 302.19 NPL Ratio by Economic Sector ........................ 312.20 NPL Ratio of Property Loans ........................... 312.21 Gross NPL Ratio of MSM and Non-MSM Loans 312.22 Results of Stress Testing Credit Risk................ 322.23 Rupiah Maturity Profile................................... 322.24 USD Maturity Profile....................................... 322.25 Results of Stress Testing Interest Rate Increases 332.26 Net Open Position (NOP) ................................ 332.27 Results of Stress Testing Rupiah Depreciation . 332.28 Results of Stress Testing a decline in SUN Price 332.29 Rupiah interest Rate Spread ........................... 342.30 Composition of Operational Profit/Loss .......... 342.31 Net Interest Income (monthly) ........................ 352.32 Share of Bank Interest Income........................ 352.33 Capital ........................................................... 352.34 Risk-Weighted Assets .................................... 352.35 Tier 1 and Tier 2............................................. 362.36 Foreign Investments (SBI, SUN and Stock) ....... 362.37 Foreign Portfolio of Rupiah Financial Assets

(SBI, SUN and Stock) ...................................... 372.38 Foreign Capital Inflows and the Exchange Rate,

IDMA Index and JSX....................................... 372.39 SUN Price of Benchmark FR Series .................. 372.40 Average Monthly SUN Price............................ 372.41 VaR SUN ........................................................ 382.42 Maturity Profile SUN ...................................... 382.43 Corporate Bond and SUN Yield (December 2009) 392.44 Corporate Bond and SUN Yield (December 2010) 392.45 Bond Yields in ASEAN (December 2009)......... 392.46 Bond Yields in ASEAN (December 2010)......... 392.47 JSX Composite as well as Global & Regional

Indices (Indexed against the position on31st December 2005)..................................... 39

2.48 Correlation between Commodities andShare Index .................................................... 41

2.49 Volatility of several Asian Bourse Indices......... 412.50 Bank Share Prices ........................................... 412.51 Changes in Bank Share Prices......................... 412.52 Performance of Mutual Funds ........................ 422.53 Net Asset Value by Type of Fund

(in trillions of rupiah) ...................................... 422.54 Capitalization Value and Value of Issuances ... 422.55 Issuances and Position of Corporate Bonds..... 422.56 Business Activity of Finance Companies.......... 432.57 Financing Growth by Finance Companies ....... 432.58 Finance Companies’ Sources of Funds ............ 43

3.1 Nominal Transactions (billions of rupiah) ........ 613.2 Transaction Volume (in thousands)................. 623.3 BI-RTGS System Transactions.......................... 623.4 BI-SSSS Transactions....................................... 633.5 National Clearing System Transactions ........... 643.6 ATM/Debit Card Transactions......................... 643.7 Credit Card Transactions ................................ 643.8 E-money Transactions .................................... 65

4.1 Institutional Cooperative Links........................ 77

5.1 Flow of Direct Investment............................... 885.2 Growth Projections for several Countries........ 88

2.1.1 LCR by Bank Group........................................ 462.1.2 NSFR by Bank Group...................................... 482.2.1 Undisbursed Loans and Liquid Funds .............. 502.2.2 Share of Undisbursed Loans by Bank Group ... 502.2.3 Share of Undisbursed Loans by Type .............. 502.2.4 Share of Undisbursed Loans by Economic Sector 512.3.1 Factors that Influence Inflation.......................... 522.5.1 Share of main sources of Income to

Operational Income (%) ................................. 562.5.2 Share of Fee Based Income to

Operational Income (%) ................................. 562.5.3 Bank Foreign Currency/Derivative Transactions

(trillions of rupiah).......................................... 57

4.1.1 Five Pillars of Financial Inclusion...................... 80

vi

Foreword

A core purpose of Bank Indonesia is to maintain financial system stability in Indonesia. This is accomplished through

macroprudential monitoring and research to comprehensively understand risks in the financial system and factors that

can trigger a crisis. The outcome of surveillance activities, which represents a form of accountability, is published in a

review, the current being the Financial Stability Review (FSR) No. 16. The printing of FSR is considered important because

through this publication the performance and future prospects of financial system stability can be communicated in detail.

In the current edition, Bank Indonesia directly appeals to market participants to take a number of measures to mitigate

potential risk in the financial sector.

An evaluation of financial system conditions revealed that financial system stability was well preserved during the

reporting period. The banking industry and financial market demonstrated impressive performance in the second half of

2010. The total assets of commercial banks swelled to Rp3,008.8 trillion, which is equivalent to 18.73% growth, buoyed by

22.8% (yoy) credit growth. Furthermore, expansion of the intermediation function was accompanied by an improvement

in banks’prudence, as evidenced by a 2.6% decline in the gross NPL ratio (as of December 2010), which took NPL to

its lowest level since the year 2000. In terms of profitability, positive bank performance throughout 2010 was coupled

with improved net profits, achieving Rp57.31 trillion; up 26.74% on the previous year. Congruent with developments

in the banking sector, business activities on the non-bank financial market escalated in the second semester of 2010.

Accordingly, the assets of financing companies expanded by 32% on the back of a 30.74% increase in financing activity.

Meanwhile, the increase in assets of pension funds was inseparable from a 46.13% rally on the Jakarta Composite Index

(JCI), considering that the composition of investment on the capital market accounted for 68.99% of total pension fund

investment.

In general, this increase in business activity by financial institutions coexisted with increasingly resolute financial

sector stability, which was reflected by a decline in the Financial Stability Index (FSI) from 1.87 in June 2010 to 1.75 in

December of the same year. Greater stability in the financial system was linked to improved macroeconomic stability and

a favourable domestic economic outlook as well as an easing of risk in the banking sector, lower bond yield spread and

less volatility on the stock market.

Notwithstanding, vigilance and caution remained necessary when addressing financial sector development. Amid

future macroeconomic conditions that are expected to continue improving, the inundation of foreign capital flows is

expected to persist, thereby, amplifying the already excessive liquidity situation. Excess liquidity must be well managed

in order to avoid the emergence of financial system instability. In addition, the possibility of a sudden reversal of short-

term foreign capital must be continuously monitored, as it would place additional pressures on the rupiah and foreign

exchange reserves.

vii

In closing, we hope that FSR will fulfil its mission as an effective media to communicate to all relevant stakeholders

the results of surveillance activities conducted by Bank Indonesia in the context of financial system stability. Moreover,

as the saying goes, there is no ivory that is not cracked, therefore, we sincerely hope to receive any comments and

suggestions to ensure future editions of FSR live up to all of our expectations.

Jakarta, April 2011

DEPUTY GOVERNOR OF BANK INDONESIA

Muliaman D. Hadad

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Overview

1

Overview

2

Overview


Overview

3

1. MACROECONOMY

The economic recovery proceeded rapidly in

emerging and developing economies, including Indonesia,

while, conversely the recovery was languid in more

advanced countries, therefore, emerging markets (EM)

remained an attractive investment destination. Abundant

liquidity on global markets encouraged foreign capital

to flow into emerging markets. In the case of Indonesia,

foreign capital inflows have raised assets prices and drove

rupiah appreciation.

The excess of liquidity increased the supply of short

term deposits which was mainly utilized as portfolio

investments. Nonetheless, excess liquidity necessitated

competent management in order to avoid the emergence

of additional risks that could undermine financial system

stability if utilised to finance imported goods that were

cheaper as a result of rupiah appreciation. Consequently,

the resultant pressures on the rupiah and foreign exchange

reserves would be compounded in the event of capital

outflows.

Potential inflationary pressures stemmed from

spiralling food and commodities prices, as well as

interregional distribution constraints. Under such

circumstances, the financial burden on households and

the corporate sector became more onerous. Furthermore,

the food and commodities price hikes weakened

corporate sector performance. Therefore, high inflation

had the potential to undermine the purchasing power

of households and the corporate sector, which would

ultimately raise credit risk in the financial system.

Bank Indonesia’s message to the banks and financial

market participants is as follows:

globally that could trigger the selling of securities

and, thereby, intensify volatility on the stock and SUN

markets. Such conditions could result from a sudden

and severe foreign capital reversal and would spur

financial system instability.

Bab 1 Overview

Financial system continues to be resilience in Indonesia during the second

semester of 2010. Amid a torrent of foreign capital inflows to Indonesia

and mounting inflationary pressures, the banking industry remained solid

by controlling the risks faced, which consequently tended to ease. Looking

ahead macroeconomic conditions are projected to improve, therefore, the

influx of foreign capital flows is expected to persist and, hence, exacerbate

excess liquidity conditions, which is one of the challenges confronting the

financial system in Indonesia. Additionally, the possibility of a sudden reversal

of short-term foreign capital requires vigilance.

4

Overview

2. INDONESIAN FINANCIAL SYSTEM

2.1. Profitability, Balance Sheets and Capital

2.2. Bank Liquidity and Funding

2.3. Risk transfer betwen Banks, Insurance

companies and Pension funds

5

Chapter 1. Risk from the Global Environment:Global Imbalances, a Torrent of Foreign Capital Inflows and rising Inflation

Chapter 1Risk from the Global Environment:Global Imbalances, a Torrent of ForeignCapital Inflows and rising Inflation

6



7


Chapter 1 Risk from the Global Environment:

Global Imbalances, a Torrent of Foreign Capital

Inflows and rising Inflation

Global imbalances remained a key contributing factor to the deluge of foreign

capital inflows to emerging market countries, including Indonesia, during

semester-II 2010. In addition, improved economic performance in Indonesia,

strong domestic demand as well as a sound and stable banking sector were

also salient determinants of the inrush of foreign capital flows. Nevertheless,

mounting inflationary pressures spurred by spiralling food (volatile foods)

and commodity prices on the international market overshadowed robust

economic performance.

The escalation of inflationary pressures did not have any significant impact on

the purchasing power of the household sector during the reporting period. In

fact, the household sector maintained adequate financial resilience, which was

reflected by the relatively small ratio of total household debt to total assets.

Notwithstanding, if inflationary pressures continue to persist the financial

burden on households will become more onerous and while income is not

experiencing a corresponding increase the level of welfare will decline. Such

conditions ultimately have the potential to undermine household purchasing

power, thereby, exacerbating credit risk in the financial system.

Soaring inflation in the second half of 2010, stemming from rising food prices,

impinged upon corporate sector performance in the third quarter of 2010,

reflected by the weaker financial performance of non-financial public listed

companies on the Indonesian Bourse, which was evidenced by a decline in

ROA and ROE.

8


1.1. SOURCES OF VULNERABILITY

1.1.1.The Global Economy and Financial Markets

The global economic recovery endured throughout

the second semester of 2010 unevenly across regions

and with a decreasing intensity. The global recovery

was signalled by an increase in global economic activity.

Manufacturing activities, exports as well as retail and

automotive sales drove economic activity in the US.

Meanwhile, manufacturing activities in Germany and

France shored up the economic recovery in Europe.

A number of risk factors slowed economic growth in

advanced countries, including high unemployment (9.4%

in December 2010) and lower property prices in the US as

well as concerns in Europe that the fiscal crises affecting

a handful of countries could spread.

Based on IMF estimates (Table 1.1), the economies of

advanced countries grew by 3.0% in quarter-IV 2010 (yoy),

much lower than that posted in emerging markets, namely

7.1%. Robust growth in emerging market countries was

attributable to strong domestic consumption in each

respective country, improved external performance in line

with the continuing global economic recovery, and soaring

commodity prices on the international market. Holistically,

global economic growth in 2010 achieved 5%.

Although the global economic landscape was mired

by uncertainty concerning the recovery in established

countries, international financial market conditions

continued to improve during the second semester of 2010

(Figure 1.1). A number of financial markets in advanced

countries, which had experienced pressures when the

crisis peaked in 2008, began to indicate much better

performance due to several factors. The economic recovery

in advanced countries, among others the United States,

United Kingdom and Japan, garnered positive sentiment

on global financial markets and gained global economic

recovery momentum. The US financial system began to

improve and annual economic growth returned to positive

subsequent to the expansionary policy of quantitative

easing1 instituted by the Federal Reserve, consisting

of additional liquidity to the tune of USD600 billion in

November 2010. Moreover, economic growth in Japan

also experienced a significant rebound, in fact posting

stronger growth than pre-crisis levels.

Problems in the financial system emerged in the

Eurozone2 as a follow-through effect from fiscal difficulties

Source: World Economic Outlook Update, January 2011. Data for Indonesia is from BPS-Statistics Indonesiaand the projections for 2010 are calculated by Bank Indonesia

2009(%)

2010(%)

Projection2010 (%)

Table 1.1Breakdown of Global Economic Growth

World Output -0.6 5.0 4.4

Advanced Economies -3.4 3.0 2.5

United States of America -2.6 2.8 2.7

Euro Area -4.1 1.8 1.7

German -4.7 3.6 2.0

France -2.5 1.6 1.8

Italy -5.0 1.0 1.0

Spain -3.7 -0.2 0.6

Japan -6.3 4.3 1.6

United Kingdom -4.9 1.7 2.0

Canada -2.5 2.9 2.3

Others -1.2 5.6 3.8

Newly Industrialized

Asian Economies -0.9 8.2 4.7

European Union -4.1 1.8 1.7

Emerging & Developing

Economies 2.6 7.1 6.5

Developing Asia 7.0 9.3 8.4

China 9.2 10.3 9.5

India 5.7 9.7 8.0

ASEAN-5 1.7 6.7 5.7

Indonesia 4.6 6.1 6.3

Latin America and

Caribbean Islands -1.8 5.9 4.3

Brazil -0.6 7.5 4.5

Mexico -6.1 5.2 4.2

1 Quantitative easing is a non-conventional policy response introduced by the central bank, which injects liquidity into the economy in order to provide economic stimuli amid disruptions stemming from a crisis.

2 EThe Eurozone incorporates those countries in the European Union that have adopted the euro as their common currency and sole legal tender.

9


Leading countries in the Eurozone, particularly

Germany, have already demonstrated a significant rebound

in economic growth, however, the periphery countries

continued to attract negative sentiment to the European

financial markets. Issues in other GIIPS countries will

continue to be monitored by the global market, hence,

handling the vulnerabilities exposed in the Eurozone

will determine the success of economic recovery in the

region.

The fiscal difficulties that have beset the Eurozone

are an invaluable lesson for the international community,

especially in terms of crisis resolution. Financial crises

require a quick and accurate resolution. A lack of fiscal

policy discipline in a number of countries resulted in a

difference of opinion in terms of resolving the crisis in the

euro area, which prolonged crisis resolution. Independent

fiscal policy among countries in the Eurozone and the

drawn out resolution of fiscal problems in one country

led to negative sentiment on financial markets in the

euro area and exacerbated the impact of the global crisis

in Europe.

This clearly provides a valuable lesson for ASEAN

member countries that have agreed to officially inaugurate

the ASEAN Economic Community in 2013. Despite strong

economic integration in the euro area, no adequate crisis

resolution mechanism was set in place that could be

executed immediately in order to dampen the crisis before

conditions on the financial markets became more severe.

Accordingly, fiscal problems triggered systemic risk on the

regional financial market. The European Central Bank (ECB)

as lender of last resort in the Eurozone was also unable to

act as the sole source of funds to resolve the fiscal crisis.

Fiscal crises in Greece (loan of £110 billion) and Ireland

(loan of £22.5 billion) were resolved through cooperation

among the European Commission, ECB and International

Monetary Fund (IMF).

3 GIIPS: Greece, Ireland, Italy, Portugal and Spain.

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

Jan

- 07

Apr -

07

Jul -

07

Oct

- 07

Jan

- 08

Apr -

08

Jul -

08

Oct

- 08

Jan

- 09

Apr -

09

Jul -

09

Oct

- 09

Jan

- 10

Apr -

10

Jul -

10

Oct

- 10

Hongkong (left scale) Dow Jones (left scale)Singapore (right scale) Inggris (right scale)

in GIIPS3 countries. Fiscal problems that peaked in Ireland

broadened CDS spread in GIIPS countries, which signaled

the existence of fiscal troubles, including in countries where

the fiscal problems were not as pronounced (Figure 1.2

and 1.3).

0

200

400

600

800

000

200

Jan

- 09

Mar

- 09

May

- 09

Jul -

09

Sep

- 09

Nov -

09

Jan

- 10

Mar

- 10

May

- 10

Jul -

10

Sep

- 10

Nov -

10

Greece

Ireland

Italy

Portugal

Spain

Germany

Source: Bloomberg (CDS USD Senior 5Y)


Source: Bloomberg

Figure 1.3CDS in leading Eurozone countries and Switzerland

Figure 1.1Global Stock Market Indices (2000=100)

Figure 1.2CDS in GIIPS countries and Germany

0

50

100

150

200

250

300

350

400

Germany

Perancis

Italy

Spain

Netherlands

Belgium

Austria

Switzerland

Jan

- 09

Mar

- 09

May

- 09

Jul -

09

Sep

- 09

Nov

- 09

Jan

- 10

Mar

- 10

May

- 10

Jul -

10

Sep

- 10

Nov

- 10

10


1.1.2. Foreign Capital Inflows to Emerging

Markets

Conditions in the Eurozone did not have a significant

impact on the regional financial market in Asia. Although

the regional financial market did experience greater

volatility when the crises in Greece and Ireland peaked,

sovereign risks in the majority of emerging market

countries in Asia did not escalate. CDS spread in a number

of Asian emerging markets actually tended to continue

declining (Figure 1.4). Consequently, the stock markets

in Asian emerging markets demonstrated the best returns

compared to bourses in other regions (Figure 1.5).

4 Conditions in emerging market countries in Europe were affected by the resolution of fiscal crises in periphery countries, while conditions in Latin America were influenced by the US recovery.

5 IIF 2011, “Capital Flows to Emerging Market Economies”, Research Note, 24th

January.

Figure 1.4CDS in several Asian Countries

0

100

200

300

400

500

600

700

800

Indonesia

Philippines

Thailand

Malaysia

Vietnam

Korea

China

Jan

- 09

Mar

- 09

May

- 09

Jul -

09

Sep

- 09

Nov

- 09

Jan

- 10

Mar

- 10

May

- 10

Jul -

10

Sep

- 10

Nov

- 10


Figure 1.5Stock Market Indices in several Countries in 2010

Source: Bloomberg

-30 -10 10 30 50

Mexico

Canada

AS S&P

AS Dow Jones

Brazil

Swedia

Germany

UK

Belanda

Swiss

Perancis

Euro Stoxx

Italy

Spain

Indonesia

Thailand

India (Karachi)

Philippines

Korea

Malaysia

Singapore

Taiwan

China (Hang Seng)

New Zealand

Vietnam

Jepang

of capital flows to emerging markets in Asia isprincipally

due to uncertainty surrounding conditions in emerging

markets in other regions, particularly Europe and Latin

America4 (Figure 1.7).

Figure 1.6Private Capital Flows – FDI and Portfolio to EM in

Asia, Europe and Latin America

Source: Institute of International Finance (IIF)5

- 50

0

50

100

150

200

250

300

350

400

2009 2010e 2011f

PMA Portofolio Loans by Banks Non-bank loans through-

Problems in established countries like the United

States, United Kingdom and Eurozone led to a concentration

of global investors focused on emerging markets, including

Indonesia. Foreign direct investment (FDI), portfolio and

bank/non-bank loans increased compared to the previous

year (Figure 1.6). Growth in emerging market countries,

especially supported by Developing Asia, achieved 7.0%.

Capital flows, particularly short-term, continued to ebb

towards emerging markets due to strong fundamentals

in countries with solid growth like China, India, Brazil

and ASEAN countries, and also because of relatively high

interest rates in these countries and the lack of investor

interest in more established countries. The concentration

11


Note: According to IIF emerging markets are grouped

into the following brackets: 1) Emerging Europe: Bulgaria,

Czech Republic, Hungary, Poland, Rumania, Russia, Turkey

and Ukraine; 2) Emerging Asia: China, India, Indonesia,

Malaysia, the Philippines, Korea and Thailand; 3) Emerging

Latin America: Argentina, Brazil, Chili, Colombia, Ecuador,

México, Peru and Venezuela; and 4) Emerging Africa/

Middle East: Egypt, Lebanon, Morocco, Nigeria, Saudi

Arabia, South Africa andUnited Arab Emirates.

The liquidity injected into the US financial market

as a part of quantitative easing by the Federal Reserve

also increased potential capital flows to emerging market

countries. From a macroprudential standpoint, the deluge

of foreign capital flows could have created an asset price

bubble and the risk of sudden reversal in emerging market

countries. Furthermore, capital inflows led to currency

appreciation in emerging market countries, which on one

hand benefitted the advanced countries to provide stimuli

for their exports. However, on the other hand a number

of emerging market countries depended on income from

their own exports that became less competitive on the

international market6. Therefore, several emerging markets

began to apply policy to reduce the unrelenting deluge

of capital inflows. Brazil, for instance, levied a tax on

capital flowing into the country. Additionally, a number of

countries like Brazil, México, Korea, Taiwan, South Africa,

Thailand, Indonesia and China used active intervention on

the forex markets to curb exchange rate appreciation.

1.1.3. Soaring International Commodity Prices

Rising commodity prices compounded the problems

experienced by the global economy. The global commodity

price index posted a significant spike (Figure 1.8). The price

of crude oil on the West Texas Intermediate(WTI) spot

market reached USD90 per barrel in December 2010. A

similar trend was also noted for commodities like gold,

copper and rubber (Figure 1.9). Referring to the UBS CMCI

composite price index, all sectors experienced price hikes,

especially the agricultural sector (Figure 1.10).

Figure 1.7Private Capital Flows to Emerging Markets

0

50

100

150

200

250

300

2009 2010e 2011f

Asia

Eropa

Amerika Latin

Source: Institute of International Finance (IIF)

6 A currency war began to emerge in October 2010. Quantitative easing, as implemented by established countries to overcome the impacts of the global crisis in 2007-2008, was one way to devalue their domestic currency.

Figure 1.8Global Price Indices

Figure 1.9Price Indices of several International Commodities

Source: Bloomberg

Source: Bloomberg

0

200

400

600

800

1000

1200

1400

1600

1800

Jan-

04

Jan-

05

Jan-

06

Jan-

07

Jan-

08

Jan-

09

Jan-

10

Jan-

11

JPMorganAggregatePrice Index

S&P GSCI Index

UBS CMCI Price Index

0

100

200

300

400

500

600

Jan

- 07

May

- 07

Sep

- 07

Jan

- 08

May

- 08

Sep

- 08

Jan

- 09

May

- 09

Sep

- 09

Jan

- 10

May

- 10

Sep

- 10

Oil Copper Gold Rubber

12


Despite assisting economic recoveries in commodity-

producing countries, soaring commodity prices also raised

global inflation. Central banks in several countries were

confronted by a dilemma of whether to raise interest rates

in order to control inflation or let interest rates remain low

to buttress the economic recovery. Raising interest rates

in emerging market countries would broaden the spread

with advanced countries, hence encouraging a further

inundation of capital flows.

1.2. STRENGTHENING RISK IN INDONESIA

1.2.1. Impact on Indonesia’s Macroeconomy

The ongoing global economic recovery coupled with

solid public purchasing power bolstered domestic economic

performance. Similar to the case in other emerging market

countries, economic performance in Indonesia during

semester-II 2010 showed adequate resilience, which in fact

tended to strengthen. Economic growth in Indonesia has

been maintained at a relatively robust level since the crisis

in 2008 up to the fourth quarter of 2010. Consequently,

the economy of Indonesia expanded by 6.14% in 2010,

which far exceeds growth posted in 2009 at 4.51%.

Domestic export performance, particularly non-oil/

gas exports based on natural resources, was buoyed by

increased global economic activityin line with high prices on

international markets. At the end of the second semester

of 2010 the value of non-oil/gas exports from Indonesia

totalled USD13.6 million; up 29.24% on the previous year.

In comparison, the value of Indonesian imports at the end

of semester-II 2010 was USD10.8 million, which represents

just 18.40% growth over the position at the end of

semester-I 2010. The higher value of exports compared to

imports led to a current account surplus of USD1.3 million

in December 2010 (end of quarter-IV 2010), which was

larger than that recorded in June 2010 (end of quarter-II

2010) of USD1.2 million (Figure 1.11 and 1.12).

Source: SEKI

Sourcer: SEKI

Sourcer: Bloomberg

Figure 1.10UBS CMCI Composite Price Index by Sector

0

500

1000

1500

2000

2500

Jan

- 04

Jul -

04

Jan

- 05

Jul -

05

Jan

- 06

Jul -

06

Jan

- 07

Jul -

07

Jan

- 08

Jul -

08

Jan

- 09

Jul -

09

Jan

- 10

Jul -

10

Jan

- 11

Agriculture Energy IndustrialPrecious Metal Livestock

Figure 1.11Indonesian Development of Non-Oil Export Import

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

Thousand USD

Non Oil Export Non Oil Import

Jan

- 07

Apr -

07

Jul -

07

Oct

- 07

Jan

- 08

Apr -

08

Jul -

08

Oct

- 08

Jan

- 09

Apr -

09

Jul -

09

Oct

- 09

Jan

- 10

Apr -

10

Jul -

10

Oct

- 10

Figure 1.12Indonesia Development of Total Exports and Imports

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

Thousand USD

Total Exports Total Imports

Jan

- 07

Apr -

07

Jul -

07

Oct

- 07

Jan

- 08

Apr -

08

Jul -

08

Oct

- 08

Jan

- 09

Apr -

09

Jul -

09

Oct

- 09

Jan

- 10

Apr -

10

Jul -

10

Oct

- 10

13


Impressive export/import performance in Indonesia

helped solidify the performance of the balance of payments

(BOP). The balance of payments registered a surplus of

USD11.3 billion at the end of semester-II 2010, which

was an increase of 109% compared to the position at

the end of the first semester totalling USD5.4 billion. In

congruence, foreign exchange reserves at the end of

semester-II 2010 increased to USD96.2 billion; equivalent

to seven months of imports and servicing foreign debt.

In line with the favourable BOP performance, the

rupiah exchange rate strengthened with low volatility.

Compared to the end of semester-I 2010 the rupiah

appreciated by 78 points, or 0.86%, by the end of

semester-II 2010 to a level of Rp8,996 per USD. The value

of the rupiah peaked in the fourth quarter at Rp8,982

(Figure 1.13). Meanwhile, average rupiah volatility against

the USD was 0.15% during the second semester of 2010,

which was far below the average volatility recorded in

semester-I 2010 at 0.31% (Figure 1.14).

The amount of investment in Indonesia in the fourth

quarter of 2010 was USD9.9 billion, which represents

a significant leap over the previous year at just USD2.5

billion. As a whole, the amount of money flowing into

Indonesia during 2010 was USD26.2 billion, up 433.7%

compared to 2009 at just USD4.9 billion. From the total

amount of investment flowing into Indonesia throughout

2010 the majority (58%) was in the form of portfolio

investment, followed by foreign direct investment (38%)

and other investment (Figure 1.15).

Source: Bloomberg (processed data)

Figure 1.13Rupiah Exchange Rate

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

2007 2008 2009 2010

Monthly average

Average quarterly

Average semester

Figure 1.14Rupiah Exchange Rate Volatility

-2

1,5

-1

0,5

0

0,5

1

1,5

2

1 12 23 34 45 56 67 78 89 100

111

122

133

144

155

166

177

188

199

210

221

232

243

254

Lower limit Upper limit Actual

Period 253 days


Figure 1.15Composition of Direct and Portfolio Investment to

Indonesia

61

4541

75

32

4539

5559

25

68

55

0

10

20

30

40

50

60

70

2005 2006 2007 2008 2009 2010

%Direct investmentInvestment Portofolio

Source: Directorate of Monetary and Economic Statistics, Bank Indonesia

14


The upward inflation trend not only affected

Indonesia but also nearly all countries, including established

and emerging market countries alike (Figure 1.18 and

1.19). Although the level of inflation was highest in

Indonesiacompared to other countries in ASEAN+5,

the investment climate in Indonesia remained attractive

considering the relatively high interest rates compared

to other ASEAN countries and advanced countries. As a

result, despite the gap narrowing between the interest rate

and inflation rate, in real terms interest rates in Indonesia

exceeded real interest rates in several other ASEAN

countries as well as in the United State and European

Union (Figure 1.20).

1.2.2. Mounting Inflationary Pressures

Inflation tended to escalate in semester-II 2010,

with headline inflation reaching 6.96% (yoy)at the end

of semester-II 2010 (Figure 1.16), in excess of its target

for 2010 at 5%±1%. The rise in headline inflation was

driven by non-fundamental factors, especially inflation of

volatile foods, which was 5.79% (ytd) in June 2010 and

peaked at 15.64% in December 2010 (ytd) (Figure 1.17).

This was primarily attributable to a lack of supply compared

to demand for staple foods. Supply was constrained by

harvest disruptions and damaged crops due to seasonal

anomalies, extreme weather and natural disasters. In

addition, high rainfall and a lack of infrastructure increased

the cost of distribution and, hence, inflated prices.

Figure 1.16Inflation in Indonesia (Core and Headline)

0,00

2,00

4,00

6,00

8,00

10,00

12,00

14,00

Core Inflation(yoy)

Inflation IHK (yoy)

Jan

- 07

Apr -

07

Jul -

07

Oct

- 07

Jan

- 08

Apr -

08

Jul -

08

Oct

- 08

Jan

- 09

Apr -

09

Jul -

09

Oct

- 09

Jan

- 10

Apr -

10

Jul -

10

Oct

- 10

Figure 1.17Price Indices of Foodstuffs and Oil in Indonesia,

2006 = 100

0

50

100

150

200

250

300

Dec -

200

6

Mar

- 07

Jun

- 07

Sep

- 07

Dec -

200

7

Mar

- 08

Jun

- 08

Sep

- 08

Dec -

200

8

Mar

- 09

Jun

- 09

Sep

- 09

Dec -

200

9

Mar

- 10

Jun

- 10

Sep

- 10

Dec -

201

0

Rice Cayenne

Petroleum Cooking Oil

Figure 1.18Inflation in several Advanced Countries

(5)

(2)

1

4

7

Jan-

07

Apr-0

7

Jul-0

7

Oct

-07

Jan-

08

Apr-0

8

Jul-0

8

Oct

-08

Jan-

09

Apr-0

9

Jul-0

9

Oct

-09

Jan-

10

Apr-1

0

Jul-1

0

Oct

-10

y.o.y %

Japan US Uni Eropa Singapore

Figure 1.19Inflation in several ASEAN Countries

(6)

(2)

2

6

10

y.o.y %

Philippines ThailandMalaysia Indonesia

Jan

- 07

Apr -

07

Jul -

07

Oct

- 07

Jan

- 08

Apr -

08

Jul -

08

Oct

- 08

Jan

- 09

Apr -

09

Jul -

09

Oct

- 09

Jan

- 10

Apr -

10

Jul -

10

Oct

- 10

Source: Directorate of Monetary and Economic Statistics, Bank Indonesia

Source: Latest indicators from the Directorate of Monetary and Economic Statistics

Source: CEIC

Source: CEIC

15


1.2.3. Risk in the Government Sector

Government finances remained stable despite the

realisation of an Rp87.9 trillion deficit in the state budget

during semester-II 2010. This is in stark contrast to the

situation the previous semester when a Rp47.9 trillion

surplus was registered (Table 1.2). The change in fortunes

was primarily the result of higher non-fuel subsidies and

more expensive cost of capital.

In order to overcome the increasingly arduous

challenges stemming from externalities that could

compromise economic stability in upcoming periods, the

government should stimulate economic growth that is

more sound and robust, namely that which is driven by

the real sector and not domestic consumption. This is

achievable by fostering stronger economic performance

through greater government capital spending.

Figure 1.20Real Interest Rates

6,0

4,0

2,0

0,0

2,0

4,0

6,0

8,0

Indonesia AS Uni Eropa Singapore

Jan

- 07

Apr -

07

Jul -

07

Oct

- 07

Jan

- 08

Apr -

08

Jul -

08

Oct

- 08

Jan

- 09

Apr -

09

Jul -

09

Oct

- 09

Jan

- 10

Apr -

10

Jul -

10

Oct

- 10

In addition, in order to mitigate the possibility of

a large sudden capital reversal on the bond market, the

government set up a bond stabilisation fund (BSF) with

an option to buy back bonds. The function of this fund

is to substitute any foreign capital withdrawn from the

bond market. The government applies three levels of BSF

to buy back bonds if conditions deteriorate through a

sudden reversal. At the first level, BSF is implemented at

the government agency or institution level in conjunction

with Bank Indonesia utilising foreign exchange reserves.

At the second BSF level funds are taken from the state

surplus (excess state revenues compared to expenditure).

Meanwhile, at the third level, the budget surplus is used

to buy back bonds through coordination with the House

of Representatives.

1.2.4. Household Sector Risk

Risk in the household sector remained low in line

with preserved macroeconomic stability and the ongoing

global economic recovery process. Based on results of

the Household Survey, in 2010 household consumption

continued to grow at a high level on the strength of

stable public purchasing power and maintained consumer

confidence. This is backed up by results of the Consumer

Survey, which shows the consumer confidence index

approaching the level of optimistic (Figure 1.21).

Source: Bloomberg,Latest indicators from the Directorate of Monetary and Economic Statistics and CEIC

APBNRp T

DetailsRp T

Realization Smt I-2010 Realization Smt II-2010

Rp T% %

Revenue and Grants 992,4 443,7 44,7% 572,8 57,7%

State expenditure 1.126,1 395,8 35,1% 660,7 58,7%

Surplus/Deficit (133,7) 47,9 (87,9)

Table 1.2State Budget Realisation for Semester I & II, 2010

16


Nonetheless, based on results of the household survey,

soaring food prices that triggered inflationary pressures at

the beginning of semester-II 2010 altered the consumption

behaviour of households. Households responded to price

hikes of their staples and daily necessities by reducing

consumption of non-basic essentials, like domestic helpers

and labourers, informal deposits, livestock and electronic

goods. Consequently, the consumption savings gained

compensated for the spiralling prices of basic staples and

foodstuffs (Figure 1.22).

From 2009-2010, a 12.95% reduction in non-basic

consumption was reported, while basic consumption

during the same period grew positively by 4.68%.

Furthermore, based on composition it is clear that

compared to 2009, fund allocation in 2010 to pay for

non-essentials experienced a decline. In 2009, food,

investment, consumption, transportation, debt repayments

and education dominated household fund allocation.

In contrast, food, debt repayments, transportation,

electricity, water and telecommunications and education

dominated in 2010. This relatively large decline in non-

basic consumption led to negative 4.27% growth in total

household expenditure.

Based on fund allocation credit risk in the household

sector remained relatively under control. Despite pressures

from rising prices, the household sector demonstrated

willing compliance to repay outstanding debt, as evidenced

by the increased fund allocation to repay debt as well as

for basic consumables and, conversely, less allocation for

non-basic consumables.

Mounting inflationary pressures also impacted

household income. Compared to 2009, total household

income declined in 2010 due to a reduction in non-basic

income like revenue from the sale of household goods,

livestock and from informal deposits. This decline in non-

basic income was as expected because in an effort to

anticipate higher prices of basic staples, households tended

to reduce their consumption of non-essential items. Non-

basic income declined by negative 33.9% from 2009 to

2010, while basic income grew by 4.1% during the same

period. As a result of the relatively large decrease in non-

basic income, total household income suffered an overall

decline of negative 1.9%.

The rise in basic household consumption as an

impact of soaring food prices is yet to significantly affect

household’s ability to pay because the decline in household

expenses exceeds the decline in income. Thus, the ratio of

Figure 1.21Consumer Confidence Index

Current Economic Conditions Index (IKE)Consumer Expectations Index (CPI)Consumer Confidence Index (CCI)

Index140

130

120

110

100

90

80

70

60 1 2 3 4 5 6 7 8 9 10 11 12

2008

1 2 3 4 5 6 7 8 9 10 11 12

2009

1 2 3 4 5 6 7 8 9 10 11 12

2010

Increasefuel Price

Economic CrisisGlobal

IncreaseTDL

Figure 1.22Composition of Household Expenditure, 2009-2010

30%

8%8%7%7%

40%

Food

Others add a property

Repayment of debt

E

Other expenses

Food

Others add a property

Repayment of debt

E

Other expenses

33%

8%7%7%6%

39%

Source: Household Survey 2009-2010

Source: Survey, Directorate of Monetary and Economic Statistics, Bank Indonesia

17


household expenses to household income also experienced

a slight decline. Based on survey data, the ratio of total

household expenses to total household income was

97.75% in 2010, which is down on the previous year at

100.64% (Table 1.3). A score of less than 100% indicates

that household income is sufficient to cover household

expenses.

1.2.5. Corporate Sector Risk

Escalating inflation potentially undermined the

performance of firms listed on the Indonesian Stock

Exchange (ISE), which limited expansionary activity

and drove up product prices, especially for companies

operating in the non-food sector and even more so for

those in the restaurants, hotels and tourism subsectors.

This potentially eroded corporate revenue, exacerbated

further by fluctuations in the exchange rate during the

reporting semester. Consequently, the performance of

non-financial companies listed on the Indonesian Bourse

The difference between income and expenses in

2010 could be utilised as household savings in order to

cover potential price hikes in upcoming periods. Consumer

survey results have shown that respondents expect the

prices of goods and services, in general, to continue rising

for the next three and six months (Figure 1.23).

In terms of the balance sheet, financial resilience

of the household sector remained sound. The household

gearing ratio remained below 5%, which is very low

compared to several other countries and indicates that

household assets still exceed household debt. Therefore,

it can be concluded that the household sector currently

maintains assets of sufficient value to cover their financial

liabilities in the event that income is no longer adequate

to repay household debt. Based on bank reports, bank

loans extended to the household sector followed an

upward trend. Meanwhile, non-performing loans in the

household sector continued to decline with a relatively

low ratio (Figure 1.24).

Ratio 2009 2010

Basic Expenses/Basic Income 55,87% 56,16%

Basic Expenses/Total Income 47,01% 50,16%

Total Expenses/Total Income 100,64% 97,75%

Table 1.3Household Expenses and Income

Figure 1.23Price Expectation Index for 3 & 6 Months

1 2

2008 2009 2010 2011

3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 10 11 12 1 2 3 4 5 63

(Index)

200

190

180

170

160

150

140

130

%

6

5

4

3

2

1

0

-1

3 months cumulative inflationEI next 3 months EI next 6 months

Note: Index = 100 indicates that respondents expect prices to remain stable; index < 100 indicates that respondents expects prices to rise; and index > 100indicates that respondents expect prices to decline

Source: Survey, Directorate of Monetary and Economic Statistics, Bank Indonesia

0

100

200

300

400

500

600

0,00%

0,50%

1,00%

1,50%

2,00%

2,50%

3,00%

3,50%

4,00%Q

IQ

IIQ

III

Q IV

Q I

Q II

Q II

IQ

IVQ

IQ

IIQ

III

Q IV

Q I

Q II

Q II

IQ

IVQ

IQ

IIQ

III

Q IV

Q I

Q II

Q II

I

2005 2006 2007 2008 2009 2010

Credit (Rp Trillion)%NPL

% NPL (left scale)Credit (right scale)

Figure 1.24Credit and NPL in the Household Sector

Source: Monthly Bank Reports, Bank Indonesia (processed data)

18


deteriorated, as reflected by declining ROA and ROE ratios

in the third quarter of 2010 compared to the same period

in the previous year. In comparison to quarter-III 2009,

ROAdeclined slightly by 2.59% to 2.15% in quarter-III

2010. Meanwhile, ROE dropped from 5.73% in quarter-III

2009 to 4.36% in QIII 2010 (Figure 1.25).

In terms of financing, the corporate sector preferred

to rely on its own capital (equity) and tended to shy away

from borrowed capital from banks or from the issuance

of other bonds and securities. This is evidenced by the

declining debt to equity ratio (DER) from 1.19 (quarter-III

2009) to 1.03 (QIII 2010) and the drop in total liabilities to

total assets (TL/TA) in the third quarter of 2010 compared

to the previous year (Figure 1.26).

Despite a slight drop in corporate performance in

terms of profitability, a number of other indicators like

the current ratio, inventory turnover ratio and collection

period remained positive. The current ratio increased from

1.37% (QIII 2009) to 1.52% (QIII 2010), while the inventory

turnover ratio trended upwards to 1.84 (quarter-III 2010).

The increase in the inventory turnover ratio indicates that

the corporate sector could efficiently manage its inventory.

Notwithstanding, the collection period experienced a

decline from 0.41 in QIII 2009 to 0.39 in the same period

of the following year, which shows that company revenues

in the form of cash declined (Figure 1.27).

Figure 1.25ROA& ROE of Non-financial Public Listed Companies

Figure 1.26DER and TL/TA of Non-financial

Public Listed Companies

-300

-200

-100

0

100

200

300

Q I

Q II

Q III

Q IV

Q I

Q II

Q III

Q IV

Q I

Q II

Q III

Q IV

Q I

Q II

Q III

Q IV

Q I

Q II

Q III

Q IV

Q I

Q II

Q III

2005 2006 2007 2008 2009 2010

% y-o-y % y-o-y

ROA (left scale)

ROE (right scale)

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

QI

QII

QIII

QIV

QI

QII

QIII

QIV

QI

QII

QIII

QIV

QI

QII

QIII

QIV

QI

QII

QIII

QIV

QI

QII

QIII

2005 2006 2007 2008 2009 2010

DER TL/TA

Figure 1.27Key Financial Indicators for the Corporate Sector

0123456

ROA

ROE

DER

2009:Q3

2010:Q3

In addition to credit risk, firms in the real sector

faced exchange rate risk. The results of stress testing 46

large conglomerates in Indonesia as of September 2010

showed that one conglomerate had negative equity. In

the event that the rupiah weakened to Rp15,500 per

USD, the performance of one conglomerate potentially

risked a 100% decline in capital. In general, conglomerates

are quite vulnerable to exchange rate fluctuations and

commodity price hikes, therefore, prudence is required

considering that 16 large conglomerates have a ratio of

net foreign liabilities to equity exceeding 25%. Conditions

have improved compared to the position in June 2010,

when 15 conglomerates had aratio of net foreign liabilities

to equity in excess of 25% (Figure 1.28). This requires

Sourcer: Bloomberg

Sourcer: Bloomberg

Sourcer: Bloomberg

19


anticipatory measures by conglomerates to mitigate the

risks (Table 1.4).

Figure 1.28Ratio of Net Foreign Liabilities to Equity

(200)

(150)

(100)

(50)

0

50

100

150

200

R P S AK T AD A O AN N AI AC Y V M I AF Q AJ AS U W K

%

Ratio of net liabilitiescurrency against the equity> 25%

Source: Indonesian Stock Exchange (processed data)

Percentage ofequity decrease

IDR / USD

10.5009.500 11.00010.000 11.500 12.000 12.500 13.000 13.500 14.000 14.500 15.000 15.500 16.000

10% 3 5 10 10 10 8 6 6 6 6 4 4 420% 1 2 2 2 5 9 8 5 4 2 3 330% 1 2 2 1 2 5 4 8 5 540% 1 2 1 2 2 5 350% 1 1 1 2 1 260% 1 1 2 170% 1 1 280% 1 90% 1 1100% 1 1

Number of corporateswith impacted equity 0 3 6 13 13 15 16 19 19 19 19 19 20 20

Table 1.4Effect of Rupiah Depreciation on Conglomerate Equity

Source: Indonesian Stock Exchange (processed data)

21

Chapter 2. Financial System Resilience

Chapter 2Financial System Resilience

22



23


Chapter 2 Financial System Resilience

Favourable economic conditions during the second semester of 2010

contributed to the preservation of financial system stability in Indonesia. Amid

an influx of foreign capital flows the banks performed amicably, coupled with

solid credit growth. Similar conditions were reported on the stock and SUN

markets, while a steady interest rate was underpinned by financing through

the capital market.

2.1. FINANCIAL SYSTEM STRUCTURE AND

RESILIENCE

The structure of the Indonesian financial system did

not experience any dramatic changes during the reporting

period. Commercial banks and rural banks continued to

dominate banking industry assets, accounting for 82.9% of

total assets in the financial sector (Figure 2.1). Meanwhile,

the total assets of the insurance and securities industry

experienced a comparatively smaller increase and, as such,

the corresponding share decreased slightly compared to

other industries like pension funds, finance companies

and pawnbrokers.The total assets of commercial banks grew by

Rp474.4 trillion (18.73%) to reach Rp3,008.8 trillion by

December 2010 on the back of 22.8% (yoy) credit growth.

Business activity on the non-bank financial market also

performed impressively during semester-II 2010. The

assets of finance companies expanded by 32% buoyed

by 30.74% growth in financing activity. Concurrently, the

increase in the assets of pension funds was linked to the

46.13% gain in the Jakarta Composite (JSX) considering

that the composition of investment on the capital market

is dominated (68.99%) by pension funds.

Greater business activity by financial institutions

was accompanied by improved financial sector stability.

Institutions Numbers

Table 2.1Number of Financial Institutions

Commercial Bank 122

Rural Bank 1,706

Finance Company 191

Insurance Company 142

Pawnbroker 1

Securities Company 119

Pension Funds 272

1.2%6.2%

5.8%0.5% 1.1% 3.3%

Banks

Rural Banks

Financing Company

Insurance Company

Pawnshop

Pension fund

81.7%

Source: various sources (processed data)

Figure 2.1Asset Compositions of Financial Institutions

24


Bank resilience and the intermediation function improved;

coupled with a lower level of credit risk, a stock price

index that reached its historical peak and a narrower bond

yield spread. Such auspicious conditions were reflected

by a declining Financial Stability Index (FSI) from 1.87 in

June 2010 to 1.75 in December of the same year (Figure

2.2). Financial system stability was inextricable from

macroeconomic stability, a propitious domestic economic

outlook as well as an easing of banking risks, a narrower

bond yield spread and less volatility on the stock market.

91,93%

61,5%

1,39%

20,3%

6,00%

8,0%

0,67%9,8%

0,4%

0%

20%

40%

60%

80%

100%

Funding Placement

Credit

DPK

Inclusion

loans

BI

Inter Bank

Inter BankSSB

SSB

2.2. BANKING SYSTEM RISK

2.2.1. Funding and Liquidity Risk

Deposits

Up to yearend 2010, bank funding depended heavily

on deposits from the general public. The share of deposits

as a source of bank funds was in the region of 92%. In

comparison, other sources of funds like interbank lending,

loans received and securities accounted for 6.00%, 1.39%

and 0.67% respectively. Robust deposit growth in the

second half of 2010 had a beneficial impact on bank

liquidity considering the outright domination of deposits

in bank funding (Figure 2.3).

Bank deposits increased by Rp242.79 trillion

(11.58%) during the second semester of 2010. Similar to

the previous year, the rise in deposits during the second

semester of 2010 was approximately two-fold compared

to the corresponding first semester (Figure 2.4). The high

realisation of the state budget during the final quarter

of each year was one contributing factor to high deposit

growth in semester-II. In addition, the inundation of

foreign capital flows to Indonesia was also thought to have

affected deposit growth in the second semester. Based on

ownership, non-resident deposits increased by 37.89%

in semester-II 2010 compared to a corresponding decline

of 1.10% in the previous semester. Despite solid growth,

the nominal rise in non-resident deposits was extremely

limited to just Rp4.51 trillion in the second semester of

2010, compared to resident deposits that expanded by

Rp238.29 trillion (Figure 2.5).

0%

3%

6%

9%

12%

15%

0

60

120

180

240

300

sem I - 08 sem II - 08 sem I - 09 sem II - 09 sem I - 10 sem II - 10

Rp T

growth in nominal - left scale

growth in % - right scale

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

20

09

M0

1

20

09

M0

7

20

10

M0

1

20

10

M0

7

20

11

M0

1

Global Crisis (Nov 2008): 2,43

1,83

1,36

AsiaFinancial Crisis1997/1998: 3,23

Mini Crisis 2005: 2,33

Dec2010: 1,75

Juny 2011 : 1,60

Source: various sources (processed data)

Source: Monthly Bank Reports, Bank Indonesia


Figure 2.2Financial Stability Index, 1996-2010

Figure 2.3Share of Bank Funding and Financing

Figure 2.4Deposit Growths by Semester

25


of statutory reserves based on the loan to deposit ratio

and the increase in the statutory reserve requirement for

foreign exchange.

7 Primary reserves include bank cask and checking accounts held at Bank Indonesia; secondary reserves include Bank Indonesia Certificates (SBI), other placements at Bank Indonesia and SUN (trading and available for sale); and tertiary reserves include SUN HTM.

(100)

0

100

200

CentralGovernment

LocalGovernment

PrivateIndividuals

Privatefinancial

institutions

PrivateCompany

OtherPrivate

Non Resident

Rp T

semester I 2010 semester II 2010

Based on component, the most impressive increase

in deposits was in the form of savings and term deposits,

which increased by 20.03% and 11.08% respectively.

Conversely, checking accounts grew by a mere 2.62%.

Despite well-maintained financial system stability

domestically, potential inflationary pressures stemming

from expectations of climbing interest rates as well as

global economic volatility could have been one factor that

steered the general public’s preference for investments

with a fixed yield, like savings accounts and term deposits.

Meanwhile, by currency, rupiah denominated deposits

continued to dominate growth in deposits. Of the total

increase in deposits during semester-II 2010, 93.13%

was attributable to rupiah based deposits. Consequently,

rupiah deposits grew by Rp226.11 trillion (12.82%) during

the reporting period compared to Rp16.68 trillion (5.03%)

for foreign currency denominated deposits.

Liquidity Risk

Strong deposit growth, in addition to benefitting

banks in terms of increasing credit, was also utilised to

manage liquidity by adding liquid assets7 (Figure 2.6).

Furthermore, banks were expected to require more

liquidity in 2010 to meet the planned implementation

600

700

800

900

0

200

400

600

Dec -09 Mar -10 Jun -10 Sep -10 Dec -10

Rp TRp T

Primary Reserves Secondary Reserves

Tertiary ReservesLiquid Assets (right scale)

As of December 2010, the total liquid assets of banks

reached Rp867.15 trillion, which represents a significant

increase compared to the position at the end of the

previous semester (June 2010), namely Rp709.28 trillion.

The most notable increase during semester-II 2010 was in

the form of primary reserves, which grew by 49.89% in line

with the mandatory increase in the primary rupiah reserve

requirement to 8% on 1st November 2010. Meanwhile,

secondary reserves increased by 18.82%. There were

indications of a shift during the reporting semester among

placements held at Bank Indonesia, namely from shorter-

term Bank Indonesia Certificates (SBI) and Term Deposits

(TD) to extended SBI (Figure 2.7).



Figure 2.5Deposit Growth based on Ownership

Figure 2.6Liquid Assets by Component

0%

20%

40%

60%

80%

100%

Jan -10 Mar -10 May-10 Jul -10 Sep -10 Nov -10

Giro Bank in BI SBI Placements with Other BI

Figure 2.7Share of Placements held at Bank Indonesia


26


With an adequate amount of liquid assets the

banks were able to better anticipate their immediate

requirements. In general, banks were resilient to a stable

decline in deposits. As of December 2010 the ratio of liquid

assets to deposits was 37.08%. Based on simulations,

no banks experienced liquidity shortfalls in the event of

deposit withdrawals up to 5% and only three banks would

potentially undergo liquidity shortfalls if 15% of total

deposits were withdrawn (Table 2.2). Such conditions were

more favourable than those endured during semester-I

2010 when three banks would potentially encounter

liquidity shortfalls if just 10% of total deposits were

withdrawn. Nonetheless, historical figures suggest that

the largest drop in deposits suffered during the crisis in

2008 was just 5%.

Referring to the liquidity index compiled by the

Basel Committee on Banking Supervision (BCBS), banks

in Indonesia generally have adequate liquidity in both the

short and long term. The Liquidity Coverage Ratio (LCR)

and Net Stable Funding Ratio (NSFR), for which the majority

of bank groups are above 1, are evidence of this (Refer to

Box 2.1: Bank Liquidity Resilience).

2.2.2. Credit Growth and Risk

Credit Growth

At the Annual Bankers’ Dinner in January 2011

the Governor of Bank Indonesia reaffirmed the need for

banks to expand the intermediation function through

credit extension. This is motivated by the fact that banks

continue to play a relatively small role in economic growth,

among others reflected by the ratio of credit to GDP at

just 27.5%. In neighbouring countries, for example, like

Thailand, Malaysia and Singapore the ratio exceeds 90%.

Therefore, Bank Indonesia persistently issues policy to

stimulate the extension of bank loans, by remaining within

prudential guidelines so that credit is allocated cautiously,

selectively and prioritised to productive sectors.

Bank credit growth in 2010 greatly exceeded that

posted in 2009, when a slowdown was experienced as

part of the fallout from the crisis in 2008/09. Bank credit

expanded by Rp179.4 trillion (11.3%) in the second

semester of 2010 compared to 10.3% in the first and

7.7% in the same period the previous year. Therefore,

year-on-year credit growth was 22.8%at year end 2010.

Such promising credit growth was linked to improvements

in economic conditions, as reflected by credit allocated as

working capital and loans in foreign currencies.

LA Ratioto Deposits

Bank Groups5% 10% 15% 20% 25% 30%

Number of banks that are not able to overcome the decline in deposits up to:

State owned 40,3% - - - - - 1

National Private 35,3% - - 1 9 15 27

Regional Development 32,6% - - 1 8 12 15

Joint Venture 30,3% - - 1 3 4 6

Foreign 40,2% - - - 2 3 4

Industry 37,08% 0 0 3 22 34 53

Table 2.2Deposits Withdrawal Simulation


27


Improved bank loan allocation was further

demonstrated by the amount of credit extended to

productive sectors. Working capital credit dominated bank

loans in semester-II 2010, growing by 15.8% (Rp120.4

trillion). When added to the 3.5% growth in investment

credit (Rp111.6 trillion) then total credit allocated to the

productive sector accounted for 73.6% of total credit

growth in semester-II 2010. This is a very gratifying

achievement considering that during the two previous

semesters the respective contributions to the productive

sector were 56.1% and 64.5% (Figure 2.8).

-5%

0%

5%

10%

15%

20%

25%

Sem I/08 Sem II/08 Sem I/09 Sem II/09 Sem I/10 Sem II/10

MSM IC CC Total

-10%

-5%

0%

5%

10%

15%

20%

25%


Mortgage Real Estate

Construction Total Property

contributed the largest growth in the property sector.

Potential growth in property credit, in particular originating

from mortgages, is still largely unrealised considering that

the nascent domestic property market is still developing.

Adequate collateral clearly provides many opportunities

for banks to expand their credit allocation.

Figure 2.9Property Credit Growth

Figure 2.8Credit Growth by Type



Improvements in credit allocation were also evidenced

in the property sector (Figure 2.9). Despite a slight decrease

in property credit growth to 7% during semester-II 2010

compared to the previous semester (7.9%), as a whole

total growth was 15.5%, which compares favourably

to the 9.7% posted in the previous year. Mortgages

Figure 2.10Credit Growth by Currency

17.1%13.7%

6.2%9.7% 10.7% 9.7%

5.0%

14.7%

-15.0%

-2.8%

8.0%

21.0%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%


Rupiah Valas

72.2% 73.1%74.2% 77.1% 75.0%

86.5%73.8

66.1% 68.1%

78.5%

0%

20 %

40%

60%

80%

100%

120%

Sem II/08 Sem I/09 Sem II/09 Sem I/10 Sem II/10

Rupiah Valas



Figure 2.11Loan to Deposit Ratio by Currency

Foreign currency denominated loans grew by 21.0%

in semester-II 2010 compared to 8.0% in the previous

semester; thus, total growth was 30.7% for the year,

which represents the highest growth in the past three

years (Figure 2.10). In fact, foreign denominated loans

actually contracted by negative 17.4% in 2009. Recent

solid growth was due to the economic recovery, which

nudged the business sector back in to life. The majority

of foreign denominated loans were allocated to the

productive sector in the form of working capital credit

28


and investment credit, with a share of 98.4% of the total.

Better export performance in 2010 also stimulated the

extension of foreign denominated loans. The share of such

loans accounted for 15.5% of total bank loans nationally.

Consequently, the LDR for foreign currencies exceeded

that for the rupiah (Figure 2.11).

The rapidity of growth in foreign currency

denominated loans is a positive sign because of its

contribution to economic growth and, subsequently,

exports. However, on the other hand banks must remain

extra vigilant when extending foreign denominated loans

due to risks stemming from fluctuations in the exchange

rate. Rupiah deprecation will increase the burden on

debtors, which could exacerbate the risks associated with

non-performing loans.

Against this conducive backdrop, the opportunity for

banks to continue expanding their credit allocation is wide

open. Of course banks will have to remain observant of

funding sources external to the banks themselves, which

are becoming more competitive, especially through the

capital and bond markets. Financing through the stock and

bond markets reached Rp280.6 trillion in 2010, which is

equivalent to 4.4% of GDP. Such conditions should drive

banks towards greater efficiency gains, thus boosting

competitiveness. The micro, small and medium (MSM)

sector provides a vast opportunity to catalyse growth in

bank loans.

MSM loans totalled Rp926.78 trillion in December

2010, growing by 10.89% in the second semester of

2010, while growth in semester-I 2010 was higher at

13.34%. According to Figure 2.12, year-on-year MSM

credit growth in December 2010 was 25.68%, which

exceeded non-MSM loans (19.77%) and even total bank

loans (22.80%). Such robust credit growth reflected strong

public demand accompanied by widespread supply from

the banks. MSM credit growth was also associated with a

lower interest rate, which in June 2010 averaged 14.49%

and subsequently declined to 14.06% in December of

the same year (Figure 2.13). The drop in the interest

rate demonstrated the banks’ commitment to stimulate

economic activity because lower interest rates prompt

demand and also alleviate the debtors’ burden.

Robust growth ensured that MSM credit dominated

total credit with a share of 52.48% in December 2010,

which is slightly smaller than the share in June 2010 at

52.68%. The share of MSM credit often fluctuates but

averaged 52.40% in 2010, which again demonstrates the

banks’ commitment to galvanise the MSME sector through

greater MSM credit allocation (Figure 2.14).

Figure 2.12MSM Credit Growth (yoy)

MSM

Non MSM

Credit

(10)

-

10

20

30

40

50

2004 2005 2006 2007 2008 2009 2010

%

12

13

14

15

16

17

18

19

2004 2005 2006 2007 2008 2009 2010

Micro Small Medium MSM



Figure 2.13MSM Lending Rates (%)

29


In terms of sources of funds, there were no

constraints to banks extending credit. Systemically, the

amount of liquid assets held by the banks was sufficient,

which could be utilised for credit rather than merely

investing in SBI and SUN. This was reflected by the

banks’ ratio of liquid assets to non-core deposits (NCD) in

December 2010 at around 184%, which is far in excess

of 100%. Furthermore, the bank loan to deposit ratio

of 75.5% also indicated that 24.5% of funds was not

invested in credit (Figure 2.15). From the perspective of

undisbursed loans, in particular those committed, which

reached Rp196 trillion in December 2010, it demonstrates

sufficient bank funding availability for loans (Refer to Box

2.2 Undisbursed Loans).

Figure 2.14Share of MSM Credit

44

46

48

50

52

54

56%

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2004 2005 2006 2007 2008 2009 2010

Total Credit Rp T (Left) MSM Rp T (Right)

% MSM/Credit (Right)

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2003 2004 2005 2006 2007 2008 2009 2010

G_Kredit

Credit allocation to government initiated projects

like infrastructure, defence equipment, agribusiness

and bio-energy was well controlled but only 54.5% of

the 2010 work program was successfully realised due

to land acquisition issues, failure to meet technical and

administrative aspects as well as postponed projects as a

result of adverse weather.

Credit Risk8

An easing of risk pressures on bank loans fostered

solid credit growth during the second semester of 2010.

The gross NPL ratio for bank loans was 2.6% in December

2010, which is lower than the position recorded in

semester-I 2010 and semester-II 2009 at 3.0% and 3.3%

respectively (Figure 2.16). Encouragingly, 2.6% represents

the lowest the gross NPL ratio has been since 2000 and

is attributable to higher quality loans accompanied by

widespread bank credit allocation. Better loan quality

stemmed from bank restructuring efforts as well as

bank write-offs. In order to anticipate mounting credit

risk pressures, banks bolstered their loan loss provisions,

thereby alleviating risk as a whole. The net NPL ratio (after

deducting loan loss provisions) was 0.3% in December

2010.


Source: Supervisory Information Management System, Bank Indonesia

Source:

Figure 2.15Loans to Deposits Ratio (LDR)

Figure 2.16Non-Performing Loans (NPL)

PPAP(left scale)NPL Nominal

(left scale)

30

35

40

45

50

55

60

65

70

75

-

1

2

3

4

5

6

7

8

9

2006 2007 2008 2009 2010

(trilion Rp)(%)

NPL Gross (right scale)

NPL Net (right scale)

8 Excluding channeling unless otherwise stated.

30


Inevitably, credit risk was a major consideration for

banks in the extension of loans. In the event of escalating

credit risk banks tended to focus more on overcoming said

risk rather than allocating more credit. This is what occurred

during the mini crisis of 2005 and the crisis in 2008/09.

Based on this experience, lower credit risk is a prerequisite

when trying to optimise bank loan allocation.

There were a number of factors that influenced

bank credit risk, including economic conditions. Changes

in economic conditions affected the policy instituted by

Bank Indonesia, for instance through changes in lending

rates, which would ultimately determine the repayment

capacity of debtors. On the other hand, stable economic

conditions directly affect the business circumstances of

debtors, which in turn will also discern repayment capacity.

Consequently, stable economic conditions are necessary to

ease bank credit risk. Of course, loans must be allocated

pursuant to prudential procedures and requirements.

Excessive credit extension without adhering to prudential

principles, even during favourable economic conditions,

will eventually intensify credit risk pressures.

The reduction in non-performing bank loans during

the second semester of 2010 stemmed from consumption

credit and investment credit with 17.4% and 17.3%

respectively (Figure 2.17). The decline in non-performing

consumption loans was primarily due to the NPL write-off

of credit cards, while that for investment credit was the

result of restructuring by the banks. The drop in non-

performing investment loans was further accompanied

by a corresponding improvement in economic conditions,

which is expected to stimulate stronger future growth in

such loans.

The significant improvement in the quality of rupiah

denominated loans was also evident for foreign currency

loans. After peaking at around 20% in 2005/06, the gross

NPL ratio of foreign currency loans in December 2010

dropped all the way to just 2.7%, only marginally higher

than the ratio for rupiah based loans at 2.5% (Figure

2.18). The easing of credit risk on foreign currency loans

contributed to strong credit growth in 2010. This increase

in foreign currency loan allocation was linked to ongoing

restructuring by the banks and reflected by the steady

decline in NPL over the past three semesters.

In general, nearly all sectors demonstrated a

downtrend in NPL ratio, with the exception of the

transportation and communications sector that experienced

an upward trend from 2.4% at yearend 2009 to 3.7%

(Figure 2.19). This increase in NPL, while remaining below

the indicative limit of 5%, requires closer attention in order

to avoid any further deterioration. Conversely, property

loans performed well in semester-II 2010, which was

indicated by a drop in total NPL for all types of property

credit (Figure2.20). Mortgages were again the type of

Figure 2.17NPL Ratio by Loan Type

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

2003 2004 2005 2006 2007 2008 2009 2010

MSM IC

CC

0%

5%

10%

15%

20%

25%

2003 2004 2005 2006 2007 2008 2009 2010

NPL ratio Rp

NPL ratio Va



Figure 2.18NPL Ratio by Currency

31


Despite posting robust growth, gross non-performing

MSM loans remained relatively low at 2.60% in December

2010, down from 2.82% in June 2010 (Figure 2.21). The

performance of gross non-performing MSM loans was

in harmony with the trend for non-MSM loans, namely

fluctuating little and below 5%. In contrast, however,

gross NPL of non-MSM loans was well above 5% during

a certain period. Consequently, the low gross NPL of MSM

loans was one of the reasons why banks tended to favour

such loans.

Looking ahead, credit risk is expected to be

maintained at a relatively low level underpinned by the

improving economy, despite a number of factors that

must be monitored relating to inflationary pressures.

Meanwhile, although the BI rate increased, its impact

will not necessarily be felt directly by bank lending rates.

Moreover, the promulgation of a new regulation ensuring

the transparent publication of the prime lending rate will

boost efficiency and bank competition, thereby curbing

interest rates. (Refer to Box 2.3 Prime Lending Rate Policy

Transparency).

The alleviation of credit risk was also indicated by the

amount of credit categorised as special mention, which

declined from semester-I 2010 to Rp83.8 trillion. Special

mention loans are those with the greatest potential to

Figure 2.19NPL Ratio by Economic Sector

0%

5%

10%

15%

20%

25%

2003 2004 2005 2006 2007 2008 2009 2010 2003 2004 2005 2006 2007 2008 2009 2010 2003 2004 2005 2006 2007 2008 2009 2010

AgricultureMiningIndustryElectricity

0%

5%

10%

15%

20%

25%

Construction

Trade

Transport

0%

5%

10%

15%

20%

25%

Business Services

Social services

Other-


Figure 2.20NPL Ratio of Property Loans

Figure 2.21Gross NPL Ratio of MSM and Non-MSM Loans

0

2

4

6

8

10

12

14

16

2003 2004 2005 2006 2007 2008 2009 2010

MortgageReal EstateConstructionTotal Property

%

2

3

4

5

6

7

8

9

10

11

12

13

14%

2004 2005 2006 2007 2008 2009 2010

Non - MSM

MSM

Source: Supervisory Information Management System, Bank Indonesia (processed data)


property loan with the lowest NPL ratio, more specifically

2.2%. Therefore, credit extended as mortgages has the

potential to continue improving.

32


become a loss. Stress tests of credit risk under a scenario

of a 2.5-fold increase in NPL (assuming 0% GDP growth)

stemming from Standard and Special Mention loans that

subsequently deteriorated to Doubtful, revealed a potential

340bps drop in CAR (Figure 2.22).

hikes (Figure 2.23 and 2.24). According to the results of

stress tests regarding changes in the interest rate, banking

industry CAR could drop by as much as 68bps in the event

of a 500bps hike in interest rates (Figure 2.25). This indicates

an improvement in bank vulnerability in comparison to

semester-I 2010 (CAR down by 105bps). Meanwhile,

more intense inflationary pressures approaching yearend

tended to encourage banks to reposition their assets in

anticipation of future interest rate risk hikes. The industry

as a whole is expected to satisfactorily manage future

increases in the interest rate.

Relatively low bank exposure to foreign currencies led

to low exchange rate risk for the banks. Bank exposure to

foreign currencies, as indicated by the net open position,

which was just 3.7% a yearend 2010, was much lower

compared to the end of 2009 (Figure 2.26). The risk of

Market Risk

Market risk was well managed during the second

semester of 2010, which contributed to the preservation

of financial system stability. Stronger concerns regarding

the debt crisis in Europe and its affect on global economic

conditions led to negative sentiment on global markets,

which overshadowed domestic economic prospects

during semester-II 2010. Nevertheless, the problems in

Europe did not significantly influence bank exposure on

and off the balance sheet. Consequently, contagion risk

from counterparty default did not affect bank capital.

Meanwhile, escalating inflationary pressures in the fourth

quarter of 2010 were felt on the domestic debt market.

Mounting inflationary pressures drove expectations

of a climbing interest rate, which had the potential to

undermine capital, particularly at banks with a short-term

funding structure (under three months).

With a bank maturity profile structure that tended

towards short in the short term and long in the long

term, banks were vulnerable to potential interest rate

Figure 2.22Results of Stress Testing Credit Risk

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

Initial CAR NPL 5% NPL 7.5% NPL 10% NPL 12.5% NPL 15%

CAR (%)

-340 bps

Figure 2.23Rupiah Maturity Profile

Figure 2.24USD Maturity Profile

-800

-600

-400

-200

0

200

400

600

up to 1 month 1-3 month 3-6 month 6-12 month >12 month

Jun-09 Dec -09 Jun-10 Dec -10

Rp T

-20

-15

-10

-5

0

5

10

15

up to 1 month 1-3 month 3-6 month 6-12 month >12 month

Jun-09 Dec-09 Jun-10 Dec-10

Billion USD


33


The SUN price increased by just 180bps during

semester-II 2010, subsequent to a significant 988bps

jump in semester-I, due to price corrections in November

(down 160bps) and December (down 96bps) followed by

rising inflationary pressures and excessively high prices.

The banks’ SUN portfolio experienced a downward

trend in the second semester of 2010, declining 5.22%

to Rp223.54 trillion (December 2010). In terms of the

composition, banks tended to decrease their available-

for-sale (AFS) and hold-to-maturity (HTM) SUN in favour

of trading SUN in line with the auspicious domestic

economic outlook. Notwithstanding, bank risk increased,

however, the majority of SUN held by banks was available

for sale (60.18%), while trading and hold-to-maturity SUN

amounted to 6.71% and 33.10% respectively.

The relatively small share of trading SUN, which was

exposed to mark to market, had a minimal impact on the

risk of lower SUN prices on bank capital. This was backed

up by stress tests that proved no bank’s CAR would drop

below the 8% limit, with CAR for the industry as a whole

only declining by 70bps in the event of a 25% dip in SUN

process. Nonetheless, when AFS SUN were included in

the simulations, the impact was more pronounced, with

CAR potential falling by 107bps (Figure 2.28). Therefore,

the 2.36% decline in SUN prices during November and

Figure 2.25Results of Stress Testing Interest Rate Increases

Figure 2.26Net Open Position (NOP)

15.80%

16.00%

16.20%

16.40%

16.60%

16.80%

17.00%

17.20%

Initial CAR Int. rate rise 1%

Int. rate rise 2%

Int. rate rise 3%

Int. rate rise 4%

Int. rate rise 5%

CAR

- 66 bps

9.75

%

3.56

%

3.15

%

6.39

%

5.87

%

2.00

%

4.5%

3.7%

2.4%

4.1% 4.

8%

4.1%

2.8%

3.9%

3.0%

4.5%

2.8%

3.1%2.

8%

2.5%

3.8%

3.8%

8.6%

3.7%

0%

4%

8%

12%

NationalPrivate

JointVenture

RegionalDevelopment

State Owned Foreign All Bank

June 2009 December 2009

June 2010 December 2010

Net Open Position

16.94%

16.95%

16.95%

16.96%

16.96%

16.97%

16.97%

16.98%

Initial CAR Depreciation Rp Depreciation Rp Depreciation Rp Depreciation Rp Depreciation Rp 10% 20% 30% 40% 50%

CAR

-2 bps

Figure 2.27Results of Stress Testing Rupiah Depreciation


exchange rate depreciation did not affect bank capital

as demonstrated by stress testing the exchange rate.

Assuming 50% rupiah depreciation, banking industry CAR

only declined by 2bps (Figure 2.27).

Figure 2.28Results of Stress Testing a decline in SUN Price

-

15.20%

15.40%

15.60%

15.80%

16.00%

16.20%

16.40%

16.60%

16.80%

17.00%

17.20%

Initial CAR Prices ofgovernmentbonds 5%

Prices ofgovernmentbonds -10%




-

CAR

107 bps


34


December 2010 (far below 25%) did not have any

significant impact on bank capital.

2.2.3. Profitability and Capital

Profitability

Positive bank performance throughout 2010 was

accompanied by increased profitability. Banks posted net

profits of Rp27.98 trillion in the second semester of 2010,

which exceed that recorded in the same period of the

previous year, namely Rp21.89 trillion. Accordingly, for

2010 as a whole, banks netted Rp57.31 trillion in profits;

up 26.74% compared to the previous year (Table 2.3).

Strong credit growth broadened interest rate spread

in 2010 and helped raise bank profitability (Figure 2.29), as

reflected by the persistently dominant share of operational

profit in total bank profit (Refer to Box 2.5 Analysis of

the main Sources of Bank Profitability). In December

2010, 65.54% of bank profit (before tax) originated

from operational profit, in particular interest income.

Conversely, the profits gleaned from foreign currency

transactions contributed a smaller amount than the

previous semester despite greater control over exchange

rate volatility in semester-II 2010 (Figure 2.29).

The domination of interest income in terms of total

bank profit was further confirmed by Net Interest Income

(NII), which averaged Rp12.48 trillion per month in 2010

compared to just Rp10.77 trillion per month in 2009

(Figure 2.31). Among the various component sources of

bank interest income, interest income from loans remained

Figure 2.30Composition of Operational Profit/Loss

(15)

(5)

5

15

Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10

Rp T

P/L Interest P/L Forex Transaction Other P/L Total Operational P/L

Figure 2.29Rupiah interest Rate Spread

4

8

12

16

20

Jan -07 Oct July -08 Apr -09 Jan -10 Oct -10

%

Dep 1 month MSM IC CC



sem I 09 sem I 10sem II 09 sem II 10Current P/Lup to Dec-09

Current P/Lup to Dec-10

Operational L/R 18,79 21,08 39,87 23,19 25,14 48,33

Non Operational L/R 12,69 9,22 21,91 16,12 11,61 27,73

Pre Tax L/R 31,48 30,30 61,78 39,31 36,75 76,06

After Tax L/R 23,33 21,89 45,22 29,33 27,98 57,31

Table 2.3Profit/Loss

Sumber: Laporan Bulanan Bank Umum (LBU), Bank Indonesia

dominant with an 81.1% share in December 2010.

Comparatively, interest income derived from placements

at Bank Indonesia and ownership of securities respectively

contributed shares of 6.75% and 8.88%. This structure

changed little from that in the previous semester (Figure

2.32).

35


Despite the increase in bank profits, the ROA ratio

declined marginally compared to the position in June 2010

(2.89%) to 2.74% in December 20101 due to solid credit

growth in the final semester. However, enhanced business

efficiency was denoted by a decline in the BOPO efficiency

ratio from 84.81% to 79.96% in the same period.

Capital

Bank capital remained relatively stable during

semester-II 2010 at around 16-17%. At the end of

semester-II 2010 bank CAR was 16.97%; down from

17.4% at the end of semester-I 2010 (Figure 2.33). The

decline in CAR was primarily due to an increase in average

risk-weighted assets that exceeded the average rise in

capital. Average capital at the end of semester-II 2010 had

risen by just 5.66%, while average risk-weighted assets

had risen by 18.29%. Total bank capital in December 2010

reached Rp330 trillion, while risk-weighted assets totaled

Rp1,944.30 trillion (Figure 2.34).

Figure 2.31Net Interest Income (monthly)

10

11

12

13

14

0

5

10

15

20

25

Jan -10 Mar -10 May -10 Jul -10 Sep -10 Nov -10

Rp TRp T

Interest Income Interest Expense NII (left scale)

Figure 2.32Share of Bank Interest Income

Credit90%

80,55% 81,11% 81,10%81,20%

6,22% 6,40%8,75% 5,45%

6,40%5,22%

6,75%8,88%

Q I 2010 Q II 2010 Q III 2010 Q IV 2010

60%

30%

0%

Placement at BI Inter Bank OthersKSB



Figure 2.34Risk-Weighted Assets

0%

5%

10%

15%

20%

25%

0

500

1,000

1,500

2,000

2,500

Dec -06 Jun -07 Dec -07 Jun -08 Dec -08 Jun -09 Dec -09 Jun -10 Dec -10

CAPITAL RWA CAR (letf scale)

Rp T

0

500

1000

1500

2000

2500

Total RWA RWA Credit RWA Market RWA Operational

Jun-09 Dec-09 Jun-10 Dec-10

Rp T

12.26%14.92%

Figure 2.33Capital



The increase in risk-weighted assets (ATMR)

principally stemmed from nominal credit ATMR amounting

to Rp194.21 trillion or 12.26% in semester-II 2010 (an

increase of just Rp7.31 trillion was reported in semester-I),

congruent to the surge in credit in the second semester.

The determinant factors (alpha) in the calculation of

operational risk were based on the Basic Indicator

Approach (BIA), which rose to 10% in July 2010, thereby

raising liabilities in the form of operational risk capital

reserves. Operational risk-weighted assets increased by

Rp48.79 trillion or 55.30% in semester-II 2010. In addition

to the increase in risk-weighted assets for operational and

credit risk, market risk weighted assets also followed an

36


upward trend, increasing by Rp9.39 trillion or 46.97% in

the second half of 2010.

The composition of bank capital in December

2010 was still dominated by Tier 1 capital (89%). The

increase in bank capital experienced in semester-II 2010

principally emanated from Tier 1 capital, which expanded

by Rp28.36 trillion or 10.69% to Rp293.68 trillion, and

Tier 2 capital that grew by Rp7.02 trillion or 24.01% to

Rp36.27 trillion. The ratio of Tier 1 capital to risk-weighted

assets in December 2010 was 15.10% (Figure 2.35). With

a relatively high composition of Tier 1 capital, banks are

expected to successfully anticipate the stricter definition

of capital under Basel III, where the components of bank

capital requirements have to be tighter, more permanent

and able to absorb losses. The results of studies, after Tier

1 capital was redefined, including regulatory adjustments

(like goodwill, intangible, deferred tax assets, cash flow

hedge reserve, investment, etc.), show a decline in Tier

1 capital of about 1.62%. Notwithstanding, the ratio

of bank Tier 1 was still adequate to meet the minimum

requirements under Basel III, namely common equity of

4.5% and tier 1 of 6%. The ratio of paid-up capital to

total capital was 57.75% in December 2010.

2.3. POTENTIAL FINANCIAL MARKET RISK AND

FINANCING

2.3.1. Potential Financial Market Risk

Foreign Portfolio: SBI, SUN and Stock

Short-term foreign capital inflows continued in

semester-II 2010, through foreign investments in rupiah

financial assets (SBI, SUN and stock) totaling Rp60.09

trillion, which brought the total for 2010 to around

Rp119.48 trillion (Figure 2.36). Nevertheless, as 2010 drew

to a close (November) significant capital outflows appeared

(Rp18.14 trillion) chiefly due to portfolio repositioning by

investors as the end of the year approached triggered by

negative international sentiment. The influx of foreign

capital flows precipitated a respective Rp13.27 trillion and

Rp33.70 trillion increase in SBI and SUN investment (Figure

2.37). Meanwhile, foreign capital also flowed on to the

stock market, indicated by net foreign stock purchases

of Rp13.27 trillion (Figure 2.38). Therefore, the share

of foreign SBI ownership at yearend 2010 was 27.45%

(15.50% in June 2010) and the share of foreign SUN

ownership was 29.93%; up from 25.76% in June 2010.

Furthermore, the portion of foreign shares increased to

32.29% from 31.36% at the end of June.

Figure 2.35Tier 1 and Tier 2

Figure 2.36Foreign Investments (SBI, SUN and Stock)

0%

5%

10%

15%

20%

25%

0

50

100

150

200

250

300

350

Dec -06 Jun-07 Dec -07 Jun-08 Dec -08 Jun-09 Dec -09 Jun-10 Dec -10

TIER 1 TIER 2 CAR (left scale)

Rp T

SBI

Q1 Q2 Q3 Q4 Q1 Q2

20102009

Q3 Q4

Rp T

22,00

18,00

14,00

10,00

6,00

2,00

-2,00

-6,00

-10,00

-14,00

-18,00

SOVERMENTS BONDS STOCK

Source: Supervisory Information Management System, Bank IndonesiaSource: Directorate of Monetary Management, Bank Indonesia

37


Bond Market

Government Bonds

The SUN price index (IDMA) reached 106.05 in

semester-II 2010, up 2.82% and bringing the total for

the year to 12.38%. The SUN market was depressed as

2010 drew to a close (November and December), with

IDMA sliding 2.37% as a result of portfolio repositioning

where investors tended to sell domestic assets in order to

realize gains for 2010 in accordance with the then recent

SUN price hike. Portfolio repositioning was triggered by

negative global sentiment. However, domestic economic

conditions remained conducive, as reflected by relatively

low interest rates and well controlled inflation, which

attracted investors and saw SUN prices spiral in 2010

(Figure 2.39). The average short (<5 years), medium (5-10

years) and long (>10 years) tenor SUN price in 2010 was

150bps, 1251bps and 1310bps respectively (Figure 2.40).

Nearing yearend 2010 the SUN market was depressed,

however, this did not affect potential risk for investors as

evidenced by a significant decline in Value at Risk (VaR) for

SUN of all tenors (Table 2.4 and Figure 2.41).

Figure 2.37Foreign Portfolio of Rupiah Financial Assets

(SBI, SUN and Stock)Rp T

55,0050,0045,0040,0035,0025,0020,0015,0010,00

5,000,00

-5,00-10,00

-0,77

Q1 Q2 Q3

2009 2010

Q4 Q1 Q2 Q3 Q4

16,63

28,6323,74

44,77

17,11

53,23

3,08

Figure 2.38Foreign Capital Inflows and the Exchange Rate,

IDMA Index and JSX

Figure 2.39SUN Price of Benchmark FR Series

15,00

10,00

5,00

0,00

-5,00

-10,00

30

20

10

0

-10

-20

-30

-40Dec 09 Jan 10 Feb 10 Mar10 May10 Sept10 Nov10Apr 10 Jun 10 July 10 Aug10 Oct10 Dec 10

TOTAL INFLOWS (trl Rp)IDMA Index (%)

CSPI (%)Rp/US$ (%)

Source: Bloomberg

Source: Bloomberg

140

130

120

110

100

90

80

70

20/12/201006/12/201022/11/201006/11/201025/10/201011/10/201027/09/201013/09/201030/08/201016/08/201002/06/201019/07/201005/07/201021/06/201007/06/201024/05/201010/05/201026/04/201012/04/201029/03/201015/03/201001/03/201015/02/201001/02/201018/01/201004/01/2010

FR0027 FR0031 FR0040 FR0050 FR0052

Source: Bloomberg

Figure 2.40Average Monthly SUN Price

125

120

115

110

105

100

95

90

Short term <5 years

Long term> 7 years

Medium-term 5 to. 7 years

2 times the average monthly

Dec’09

Jan’10

Feb’10

Mar’10

Apr’10

May’10

Jun’10

Jul’10

Aug’10

Sep’10

Oct’10

Nov’10

Dec’10

Source: Bloomberg

38


Corporate Bonds

Market perception of the risk profile of corporate

bonds remained relatively high, on average 397bps above

the SUN yield, compared to 195bps above the SUN yield

in 2009 (Figure 2.43 and 2.44). The wider yield spread

between SUN and corporate bonds was due to Moody’s,

Fitch and the Japan Credit Rating Agency all upgrading

their rating of government bonds.

Government issuances of SUN increased by 3.22%

to Rp641.21 trillion in 2010 with a declining share of

bank ownership to 34.23% (37.45% in 2009), while the

share of foreign SUN ownership expanded (Table 2.5).

From a liquidity standpoint, the government was able to

exploit the steady interest rate and strengthen SUN market

liquidity through the issuance of longer-tenor SUN (Figure

2.42). Consequently, SUN market liquidity became more

concentrated on long-tenor SUN. At the end of 2010 the

share of short-tenor SUN was 7.37% (7.83% in 2009),

medium-tenor SUN was 5.10% (8.03% in 2009) and

long-tenor SUN was 86.53% (85.06% in 2009).

Table 2.4VaR SUN

TenorShortTerm

MiddleTerm

LongTerm

Dec 09 0.757 1,293 1,560

Jan 10 0,625 1,201 1,412

Feb 10 0,514 1,028 1,229

Mar 10 0,399 0,846 0,908

Apr 10 0,384 0,835 0,919

May 10 0,384 0,899 1,001

June 10 0,361 0,808 0,930

July 10 0,354 0,792 0,913

Aug 10 0,304 0,724 0,883

Sept 10 0,315 0,704 0,882

Oct 10 0,309 0,685 0,962

Nov 10 0,323 0,702 1,137

Dec 10 0,338 0,720 1,191

Figure 2.41VaR SUN

Figure 2.42Maturity Profile SUN

1,800

1,600

1,400

1,200

1,000

0,800

0,600

0,400

0,2000,000

Dec 09 Jan 10

Short Term Long Term Long Term

Feb 10 Mar 10 Apr 10 May 10 Jun 10 Jul 10 Aug 10 Sept 10 Oct 10 Nov 10 Dec 10

60

50

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2030

2031

2037

2038

40

30

20

10

Variable Rate

Rp T

Fixed Rate



Source: Bloomberg

Tabel 2.5SBN Ownership

Ownership

Rp T

Jun’10 Des’10 Change

SBN Ownership (Nominal)

Banking 232,67 219,52 -13,15

BI 19,12 15,62 -3,5

Mutual Fund 48,84 51,16 2,32

Insurance 77,44 79,3 1,86

Foreign 162,05 195,3 33,25

Pension Fund 36,48 36,75 0,27

Securities 0,13 0,13 0

Others 44,49 43,43 -1,06

Total 621,22 641,21 19,99


39


In the ASEAN region, Indonesia government bonds

were hit hard and experienced significant declines across

all tenors, with 10-year-tenor SUN affected most harshly,

namely a drop of 260bps (Figure 2.45 and 2.46). Despite

the setback, the yield of government bonds in Indonesia

remained the highest in the region and, thus, attractive

to investors in the short term.

Stock Market

The JSX Composite continued to strengthen in

semester-II 2010 to a level of 3,703.51 (up 27.11%),

bringing the total increase for the year of 2010 to 46.13%

(Table 2.6 and Figure 2.47). On 11th September 2010

foreign capital flowed out of Indonesia triggered by

negative sentiment that spurred inflationary pressures,

hence the index sunk to its nadir of 3,531.21. Global

bourses in 2010 strengthened due to positive sentiment

surrounding government stimuli and low interest rates

maintained by the Federal Reserve, European Central Bank

and Bank of Japan. Meanwhile, concerns over the debt crisis

in Europe eased after the ECB agreed a bailout package for

the countries involved and reiterated its commitment to

buy government bonds from the European Union, which

led to positive sentiment on global bourses.

Figure 2.43Corporate Bond and SUN Yield (December 2009)

Figure 2.44Corporate Bond and SUN Yield (December 2010)

Figure 2.45Bond Yields in ASEAN (December 2009)

Corporation SUN

14

12

10

8

6

4

2

01 2 3 4 5 6 7 8 9 10 11

Corporation SUN

14

12

10

8

6

4

2

01 2 3 4 5 6 7 8 9 10 11

12

10

8

6

4

2

01

Indonesia Malaysia

Philippines Singapore

2 3 4 5 6 7 8 9 10 15 20 25 30

Source: Bloomberg

Source: Bloomberg

Source: Bloomberg

Figure 2.46Bond Yields in ASEAN (December 2010)

12

10

8

6

4

2

01

Indonesia Malaysia Philiphina Singapore

2 3 4 5 6 7 8 9 10 15 20 25 30

Sumber: Bloomberg

Figure 2.47JSX Composite as well as Global & Regional Indices

(Indexed against the position on 31st December 2005)

Des 09

Jan 10

Feb 10

Mar 10

Apr 10

Mey 10

Jun 10

Jul 10

Agt 10

Sep 10

Oct 10

Nov 10

Dec 10

3.20

2.70

2.20

1.70

1.20

0.70

IHSGPCOMP

FTSE

FSSTINKYNYA

SETHang Seng

KLCIKOSPI

DJIA

Source: Bloomberg

40


A recovery in the global economy coupled with a

stable domestic interest rate, which encouraged a further

deluge of short-term foreign capital inflows, lowered

the risk perception of emerging market (EM) countries.

The Japan Credit Rating Agency upgraded Indonesia’s

sovereign rating from BB+ to BBB-, as well as the foreign

currency long-term senior debt and local currency long-

term senior debt ratings from BB- to BBB, each with a

stable outlook. Meanwhile, Fitch Ratings upgraded their

long-term foreign and local-currency credit ratings for

Indonesia to BB+ from BB and Moody’s raised their local

currency bond rating for Indonesia from Ba2 to Ba1, which

boosted positive sentiment domestically and strengthened

the stock market.

By sector, the JSX composite rallied primarily on

the strength of shares in the trade sector (up 49.54%),

mining (up 46.24%) and agriculture (up 37.57%). Sectoral

performance indicated that commodity-based shares were

the main stanchions of gains on the JSX (Table 2.7 and 2.8).

Active transactions on international commodity markets,

which pushed up international commodity prices, also had

a tangible effect on the JSX composite.

Observations of historical data since 2000 indicate

a positive correlation between the JSX composite and

nearly all international commodities. The correlation

was strongest with the prices of crude oil and palm oil

(Figure 2.48), especially during the 2008 crisis. The sub-

prime mortgage crisis in 2008 caused global investors to

switch to commodity markets, which rapidly drove up

international commodity prices. This ultimately became

Table 2.6Indices of several Global Stock Markets

Dec 09 Jun 10 Dec 10 Sem II 10

Growth

Dec09-Dec10

JCI 2.534,36 2.913,68 3.703,51 27,11% 46,13%

FSSTI 2.879,76 2.835,51 3.190,04 12,50% 10,77%

SET 734,54 797,31 1.032,76 29,53% 40,60%

KLCI 1.271,12 1.314,02 1.518,91 15,59% 19,49%

PCOMP 3.052,68 3.372,71 4.201,14 24,56% 37,62%

NKY 10.546,44 9.382,64 10.228,92 9,02% -3,01%

Hang Seng 21.496,62 20.128,99 23.035,45 14,44% 7.16%

KOSPI 1.682,77 1.698,29 2.051,00 20,77% 21,88%

UKX 5.397,86 4.916,87 7.964,02 61,79% 47,54%

NYA 7.241,24 6.469,65 5.899,94 -8,81% -18,52%

DJIA 10.548,51 9.774,02 11.577,51 18,45% 9,75%

Table 2.8Jakarta Composite Index (Commodities)

JCI

Crude Oil 0,291

Aluminum 0,231

Copper 0,229

Tin 0,186

Gold 0,097

Palm Oil 0,272

Rubber -0,035

Coffee 0,210

Rice 0,110

Plywood 0,110

Tea 0,047

JCI 1,000

Table 2.7Share Price Indices by Sector

Dec09

Jun10

Dec10 Sem II 10

Growth

Dec09-Dec10

JCI 2.534,36 2.913,68 3.703,51 27,11% 46,13%

Financial 301,42 377,18 466,67 23,72% 54,82%

Agriculture 1.753,09 1.660,50 2.284,32 37,57% 30,30%

Basic Industry 273,93 312,02 387,25 24,11% 41,37%

Consumtion 671,31 959,04 1.094,65 14,14% 63,06%

Property 146,80 163,38 203,10 24,31% 38,35%

Mining 2.203,48 2.238,86 3.274,16 46,24% 48,59%

Infrastructure 728,53 678,12 819,21 20,81% 12,45%

Trade 275,76 317,02 474,08 49,54% 71,92%

Miscellaneous Industries 601,47 809,20 967,02 19,50% 60,78%

Source: BloombergSource: Bloomberg

Source: Bloomberg

41


positive sentiment that precipitated a corresponding rally

on the JSX composite during the same period.

Mutual Funds

The amount of mutual funds declined in semester-II

2010 from 598 in June 2010 to 558 in December (Figure

2.52), however, performance improved as demonstrated

by a 21.61% jump in net asset value (NAV) compared

to just 8.52% in semester-I. Consequently, the net asset

value of mutual funds in 2010 increased by 31.97% with

a corresponding 16.88% rise in the number of investment

units. The NAV of mutual funds rose principally due to

discretionary funds and equity funds, which increased

respectively by 60.62% and 25.09% to Rp21.99 trillion

and Rp46.67 trillion. In addition, the net asset value of

Indonesia

Japan

Thailand

Malaysia

Singapore

Hongkong

%45

40

35

30

25

20

15

10

5

0

Dec-09Jan-10

Feb-10Mar-10

Apr-10May-10

Jun-10Jul-10

Aug-10Sep-10

Oct-10Nov-10

Dec-10

Figure 2.48Correlation between Commodities and Share Index

Figure 2.49Volatility of several Asian Bourse Indices

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Crude Oil

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Palm Oil



Volatility on the domestic bourse in semester-II 2010

eased from 32.88 (June 2010) to 23.82 (December 2010)

principally because of price corrections, in particular as

yearend approached (Figure 2.49). Compared to several

Asian bourses, the highest volatility index affected shares in

the mining sector, which signaled the impact of speculative

behavior and short-term profit taking by investors.

Shares in the financial sector, particularly the

banking sector, rallied due to widespread investor interest

during 2010 (Figure 2.50 and 2.51). The following banks’

share prices posted gains during the second semester of

2010: BCA (up 31.96%), Mega (up 38.04%), Niaga (up

169.01%), Permata (up 123.75%), Panin (up 50.00%),

3000

2500

2000

1500

1000

500

Niaga (LHS)

Bukopin (LHS)

BRI (RHS)

Permata (LHS)

BCA (RHS)

Danamon (RHS)

Panin (LHS)

Mega (RHS)

BNI (RHS)

BII (LHS)

Mandiri (RHS)

NISP (LHS)

0

14000

12000

10000

8000

6000

4000

2000

0

08/07/

2010

18/07/

2010

28/07/

2010

07/08/

2010

17/08/

2010

27/08/

2010

06/09/

2010

16/09/

2010

26/09/

2010

06/10/

2010

16/10/

2010

26/10/

2010

05/11/

2010

15/11/

2010

25/11/

2010

05/12/

2010

15/12/

2010

25/12/

2010

Figure 2.51Changes in Bank Share Prices

Figure 2.50Bank Share Prices

Sem II’10

80.00%

70.00%

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00%

-10.00%

-20.00%BCA Mega Niaga Permata Panin BII Mandiri Bukopin BRI BNI NISPDanamon

Sem I’10

Source: Bloomberg

Source: Bloomberg

BII (up 136.36%), Mandiri (up 38.30%), Bukopin (up

73.33%), BRI (up 37.25%), Danamon (up 25.27%), BNI

(up 95.71%) and NISP (up 70.00%).

42


Equity

Fixed Income

ETF-Equity

Capital Market

Protected

ETF-Fixed Income

Discretionary

Indexed

Islamic

50.00

40.00

30.00

20.00

10.00

0.00

Jun 10 Jul 10 Aug 10 Sep 10 Oct10 Nov 10 Dec 10

160.00

Jun 10

NAV, trl Rp No. of Matual FundsNo. of Stocks/Unit-Billion

Jul 10 Aug 10 Sep10 Oct10 Nov10 Dec10

140.00

120.00

100.00

80.00

60.00

40.00

20.00

0.00

610

600

590

580

570

560

550

540

530

fixed-income funds and protected funds grew specifically

by 48.95% and 21.34% to Rp27.27 trillion and Rp42.01

trillion (Figure 2.53). Accordingly, the largest NAV share

remained dominated by equity funds.

growth was reported (Figure 2.54). An additional 24

companies began issuing shares bringing the total to 521.

Meanwhile, corporate financing through the issuance

of corporate bonds also expanded, although not quite

of the same magnitude as share issuances. The value of

corporate bonds issued in semester-II 2010 surged 14.81%

to Rp187.38 billion (up just 7.84% in semester-I). The total

number of companies issuing corporate bonds grew in

2010 from 183 (December 2009) to 189 (Figure 2.55). Ten

firms issued corporate bonds during semester-II 2010 with

a value of Rp15.40 trillion. As a result, 26 companies issued

corporate bonds in 2010 to the tune of Rp36.60 trillion.

Some issuers, particularly finance companies, issued

through refinancing. A total of seven finance companies

issued bonds in 2010 with a value of Rp8.6 trillion.

2.3.2. Financing through the Capital Market and

Finance Companies

Issuers of Shares and Corporate Bonds

Corporate financing through the issuance of shares

continued to rise in semester-II 2010 with a 13.12%

increase in value amounting to Rp495.4 trillion. Therefore,

in 2010 as a whole the value of share issuances increased

by 18% compared to the previous year when just 6%

Figure 2.52Performance of Mutual Funds

Figure 2.53Net Asset Value by Type of Fund

(in trillions of rupiah)

Figure 2.54Capitalization Value and Value of Issuances

3,500.0

3,000.0

2,500.0

2,000.0

1,500.0

1000.0

(in

trl

Rp

)

500.0

000.0

4,000.0

3,500.0

3,000.0

2,500.0

2,000.0

1,500.0

1,000.0

500.0

0.0Dec09

Jan10

Feb10

Mar10

Apr10

May10

Jun10

Jul10

Aug10

Sep10

Oct10

Nov10

Dec10

N Cap (BEI) N Emission

CSPI (RHS)

Source: Capital Market Statistics, the Capital Market and Financial Institution Supervisory Agency




Figure 2.55Issuances and Position of Corporate Bonds

Dec09

190

189

188

187

186

185

184

183

182

181

180

200

250

Rp T

150

100

50

0Jan10

Feb10

Mar10

Apr10

May10

Jun10

Jul10

Aug10

Sep10

Oct10

Nov10

Dec10

Emission (LHS) Emission (RHS)

43


Finance Companies

In addition to banks and the capital market, a source

of pressure that can disrupt financial system stability is from

non-bank financial institutions like finance companies.

Finance companies are one type of non-bank financial

institution that provides various forms of financing, for

example leasing, factoring, consumer finance and credit

cards. The down trending domestic interest rate since 2009

and its relatively stability in 2010 has expanded the role

of non-bank financing, among others, through finance

companies.

The business activity of finance companies increased

in semester-II 2010 (Figure 2.56). Total assets were

up 14.25% to Rp230.3 trillion, but growth was lower

than that posted in the previous semester totaling

14.50%. Increased funding and capital to the amount

of 23.03% and 7.02% respectively during the reporting

semester underpinned the expansion of business activity.

Concentration risk remained high at finance companies

because of the four types of financing offered, most activity

concentrated on consumer financing, for which the share

increased from 68.19% in June 2010 to 69.77% of total

financing in December (Figure 2.57). This hadthe potential

to aggravate credit risk because the primary funding source

of finance companies was bank loans.

Funding sources of finance companies continued to

rely on domestic bank loans during the reporting semester,

with a 51.97% share equivalent to Rp85.05 trillion. While

the share remained lower than domestic bank loans, funds

sourced from foreign bank loans and bond issuances were

increasingly in demand, growing by 24.36% and 15.15%

respectively (Figure 2.58). Seven finance companies issued

bonds during semester-II 2010 with a total value of

Rp8.6 trillion, namely BFI Finance Indonesia, Astra Sedaya

Finance, BCA Finance, Federal International Finance, Oto

Multiartha, Adira Dinamika Multifinance, and Summit Oto

Multifinance. The majority of funds from bonds (90%)

were allocated to consumer financing, particularly to

finance new and secondhand automobiles.

Figure 2.56Business Activity of Finance Companies

0.00

50.00

100.00

150.00

200.00

250.00

Asset Financing Funding Capital

December 2009 June 2010 December 201014,25%

14,19%

23,03%

7,02%

Source: Data from the Directorate of Economic and Monetary Statistics, Bank Indonesia

Figure 2.57Financing Growth by Finance Companies

Leasing Factoring Credit Card CostumerFinancing

TotalFinancing

Dec'09 46,528 2,027 930 93,054 142,539Jun'10 48,985 2,084 854 111,279 163,201Dec'10 53,167 2,295 876 130,016 186,354

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000 up 14,19%

8,54%

10,15% 2,57%

16,84%


Figure 2.58Finance Companies’ Sources of Funds

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

DomesticBank Loans

ForeignBank Loans

SecuritiesIssued

Total Sourceof Found*

Dec'09

Jun'10

Dec'10

23,93%

24,36%

15,15%

23,03%

In Billion Rp


44


The performance of finance companies improved in

semester-II 2010 reflected by a 5.02% increase in ROA

compared to 2.91% in June 2010 and a 24.17 increase

in ROE compared to 13.53% in June 2010. Despite an

improvement in performance, finance companies were

yet to enhance their business efficiency as indicated by an

increase in the BOPO efficiency ratio from 71.44% in June

2010 to 73.94% in December 2010 (Table 2.9).

Encouragingly, the credit risk of finance companies

eased, as demonstrated by declines in nominal NPL and

the NPL ratio from Rp2.9 trillion or 1.72% in June 2010

to Rp2.6 trillion or 1.37% in December 2010 respectively

(Table 2.10). This decline was primarily spurred by a

reduction in the nominal NPL and NPL ratio of leasing and

factoring. Stronger economic growth was one driver of

debtor repayment capacity, thereby reducing the nominal

non-performing loans of finance companies. Conversely,

rapid consumer financing growth was followed by a

corresponding increase in nominal NPL despite a drop in

the NPL ratio from 1.76% (June 2010) to 1.63% (December

2010). This was caused by consumer financing growth

outpacing the increase in nominal NPL. The aggregate

growth in automotive sales reached 60% compared to

2009, which also catalyzed consumer-financing activity

by finance companies.

The number of finance companies affiliated with

banks declined from 25 in June 2010 to 23 in December

2010. The remaining finance companies continued to focus

their activities on consumer financing. Of the 23 finance

companies, seven experienced an increase in their NPL ratio

and six experienced a decline (Table 2.11).

December

2009

June

2010

December

2010

December

2009

June

2010

December

2010

Asset 174,442 201,570 230,301

Debt 115,555 133,057 163,701

Obligation 134,354 158,180 182,470

Equity 40,088 43,390 47,831

Profit Before Tax 10,421 5,869 11,563

Profit After Tax 7,827 4,637 8,929

ROA (%) 5,97 2,91 5,02

ROE (%) 25,99 13,53 24,17

BOPO (%) 73,81 71,44 73,94

Debt/Equity 2,88 3,07 3,42

Obligations/Equity 3,35 3,65 3,81

Table 2.9Financial Ratios of Finance Companies

Table 2.10NPL of Finance Companies

Leasing 730,03 714,49 350,91

Factoring 126,34 123,01 73,16

Credit Cards 40,84 47,01 44,05

Consumer Financing 1.932,14 2.016,96 2.189,40

Total Financing 2,829,34 2.901,47 2.657,51

% NPL Dec 2009 June 2010 Dec 2010

Leasing 1,50% 1,39% 0,63%

Factoring 5,93% 5,60% 3,07%

Credit Cards 3,93% 5,00% 4,62%

Consumer Financing 2,01% 1,76% 1,63%

Total Financing 1,91% 1,72% 1,37%



Billion Rp

Billion Rp

45


Table 2.11NPL Performance of Finance Companies

NPL

Jun 10 Dec 10 Sem II 10 Leasing FinanceCompanies

Growth ofFinanceActivity

Factoring Crit Carded

Change of NPL Nominal (in Thousand Rp)

1 16,22% 4,14% (342,318) - - - -5,79%2 0,40% 0,31% - - - 6,019 78,76%3 0,00% 0,00% - - - - - 75,12%4 0,00% 0,00% - - - - - 88,05%5 0,25% 2,45% 210 2,191 - 827 -5,09%6 0,83% 0,85% 7,792 - - (557) 39,20%7 6,08% 0,57% - - - (2,534) 22,21%8 0,08% 0,00% - - - (516) 25,23%9 0,03% 0,06% - - - 712 27,16%

10 1,32% 1,42% - - - 13,546 31,63%11 0,00% 0,00% - - - - - -22,51%12 0,00% 0,00% - - - - - 15,78%13 0,00% 0,00% - - - - - 54,89%14 0,00% 0,00% - - - - - 12,65%15 0,51% 0,50% (50) - - - 1,19%16 0,24% 0,48% - - - 1,919 63,38%17 0,00% 0,00% - - - - - 7,86%18 0,07% 0,06% 88 - - 6 55,67%19 0,03% 0,10% - - - 2,488 14,4%20 0,00% 0,00% - - - - - 7,52%21 0,00% 0,00% - - - - - -21,88%22 22,27% 23,68% (54) - - 307 -6,55%23 0,00% 0,00% - - - - - 19,35%


46


Box 2.1 Bank Liquidity Resilience

Referring to liquidity indicators compiled by The

Basel Committee on Banking Supervision (BCBS), in

general banks in Indonesia have an adequate level of

liquidity resilience in both the long and short term. The

Liquidity Coverage Ratio (LCR) and Net Stable Funding

Ratio (NSFR) demonstrate this, which are both above 1

for the majority of bank groups with the exception of

NSFR for foreign banks. This illustrates that the majority

of banks are able to meet the expected requirements

should LCR and NSFR be applied to banks in Indonesia.

Nevertheless, bearing in mind the amount of detailed

data required to calculate LCR and NSFR, adequate

preparations are required in terms of risk management

and information technology.

Background

The global financial crisis in 2007/08 placed

liquidity risk under a whole new perspective, indeed

playing a pivotal role in financial system stability. In

order to enhance liquidity risk management and control

liquidity risk exposure, the Basel Committee on Banking

Supervision (BCBS) released Basel III: International

Framework for Liquidity Risk Measurement, Standards

and Monitoring in December 2010, which proposes the

use of two ratios to monitor liquidity conditions.

The ratios proposed are the Liquidity Coverage

Ratio (LCR) and the Net Stable Funding Ratio (NSFR).

LCR indicates short-term liquidity resilience by

addressing the sufficiency of high-quality liquid assets

to cover net cash outflows for a 30-day period under a

stress scenario. This metric aims to encourage banks to

enhance short-term liquidity resilience by ensuring an

adequate level of unencumbered, high-quality assets.

Meanwhile, NSFR is the ratio of long-term funding

sources (< 1 year), known as stable funding, owned by

the bank compared to sources of stable funding that

must be maintained to support investment in liquid

assets, credit and off balance sheet exposure. NSFR

seeks to enhance long-term bank funding resilience

by encouraging banks to broaden their sources of

long-term funding.

Long-Term Liquidity Resilience (LCR)

In general, short-term bank liquidity resilience

in Indonesia is sufficient, with an average liquidity

coverage ratio in excess of 1.


Figure Box 2.1.1LCR by Bank Group

3,00

2,50

2,00

1,50

1,00

0,50

Jan-10

State owned National Private Regional Development Joint Venture Foreign

Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10

-

47


The liquidity coverage ratio of banks during

2010 was, on average above 1, within the range of

1.26 to 1.97. This illustrates that banks in Indonesia

have sufficient liquid assets to cover any possible fund

withdrawals for the upcoming 30 days under stress

conditions. More specifically, the relatively large amount

of high-quality liquid assets maintained by private banks

ensured that this group of banks had the highest LCR,

namely 2.12. Conversely, foreign banks proved to have

the lowest LCR at 1.26.

Average LCR for regional development banks

exceeded that for state-owned banks with 1.81 and

1.65 respectively. In contrast, average LDR for regional

development banks was lower than that for state-

owned banks totalling 74.24 and 83.43 respectively.

Such conditions indicate that compared to state-owned

banks, regional development banks tend to invest more

funds in low-risk liquid assets.

Long-Term Liquidity Resilience (NSFR)

The long-term liquidity resilience of banks is

indicated by an average net stable funding ratio of

above 1. With the highest net stable funding ratio of

any bank group coupled with a ratio of liquid assets

to credit totalling 61.53%, private banks have the best

potential to enhance their intermediation function.

The average net stable funding ratio surpassed 1

for all bank groups except foreign banks, which implies

that, in general, long-term bank funding sources can

cover bank investments in liquid and non-liquid assets.

Private banks had the highest average net stable

funding ratio, namely 2.04, with an average ratio of

liquid assets (placements at Bank Indonesia and SUN)

to credit totalling 61.53%. This denotes the proclivity

of private banks towards long-term investment in low-

risk assets. Meanwhile, foreign banks had the lowest

Table Box 2.1.1LCR by Bank Group

LCR Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10

State Owned Banks 1,96 1,78 1,65 1,60 1,51 1,62 1,48 1,44 1,53 1,64 1,63 1,97

Commercial Banks 2,77 2,53 2,39 2,25 1,96 1,89 1,91 1,82 1,92 1,99 2,04 1,95

Regional Development 1,80 1,79 2,93 1,83 1,77 1,99 2,00 1,83 1,92 1,89 1,75 1,24

Joint Venture Banks 1,31 1,26 1,35 1,07 1,22 1,11 1,34 1,37 1,14 1,45 1,44 1,26

Foreign Owned Banks 1,31 1,16 1,31 1,09 1,34 1,28 1,32 1,23 1,16 1,21 1,44 1,26

Table Box 2.1.2NSFR by Bank Group

NSFR Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10

State Owned Banks 1,60 1,52 1,31 1,50 1,46 1,49 1,50 1,49 1,50 1,52 1,48 1,60

Commercial Banks 2,62 2,16 1,40 1,99 1,95 2,19 1,96 1,97 2,03 2,06 2,05 2,11

Regional Development 1,76 1,81 1,61 1,87 1,80 1,88 1,88 1,79 1,85 1,81 1,76 1,62

Joint Venture Banks 1,43 1,44 0,91 1,41 1,35 1,44 1,67 1,71 1,76 1,62 1,42 1,72

Foreign Owned Banks 1,15 1,08 0,66 1,05 0,98 0,88 0,87 0,92 0,79 0,84 0,77 0,74



48


Figure Box 2.1.2NSFR by Bank Group

NSFR at just 0.89 with a ratio of liquid assets to credit

of 77.62%. This indicates that foreign banks tend to

favour short-term funds to invest in low-risk assets.

State-owned banks and regional development

banks had an average net stable funding ratio of 1.50

and 1.79 respectively and a ratio of liquid assets to

credit of 41.15% and 41.85%, which indicates that

the funding sources of regional development banks are

dominated by stable funding. Oppositely, state-owned

banks tend to prefer short-term funding (< 1 year).Source: Monthly Bank Reports, Bank Indonesia (processed data)

49


Box 2.2 Undisbursed Loans

Solid credit performance was achieved in 2010,

accompanied by an increase in undisbursed loans (UL).

During the second semester of 2010 undisbursed loans

increased by 15.5%, hence the share of UL in total

bank credit was 31.8%.

Undisbursed loans are a credit facility agreed

by the bank and offered to a debtor, however, the

debtor does not immediately use the facility offered

due to gradual withdrawal of because of other

reasons. Increases in undisbursed loans cannot be

avoided because they correlate closely with credit

commitments, particularly credit that can be withdrawn

in stages.

Limited bank understanding of a debtor’s

business activity due to asymmetric information

prevents banks from calculating the real requirements

of a debtor. Consequently, the credit facility agreed is

often excessive compared to the actual requirement.

From the debtor’s standpoint, a request for credit

that exceeds the actual requirement is intended as a

reserve. The opportunity to expand undisbursed loans

increases if the atmosphere of uncertainty thickens

and, consequently, the debtor envisages future

business disruptions, hence, demanding greater credit

commitment but without any actual increase in real

funding needs.

A change to the way undisbursed loans

are reported by banks occurred in January 2010.

Accordingly, banks now have to report both

committed and uncommitted undisbursed loans,

the aim of which is to ensure that banks are more

transparent when reporting their undisbursed

loans and, hence, can better analyse their risks. For

committed undisbursed loans, banks must specify

their quality, which entails additional provisions for

earning asset losses in accordance with the quality

specified. However, this policy does not apply to

uncommitted undisbursed loans. As a result of

the change in reporting, total undisbursed bank

loans skyrocketed in comparison to yearend 2009.

In December 2010, total undisbursed bank loans

reached Rp561.2 trillion or 31.8% of total bank

credit. However, of this total only Rp196 trillion was

committed, equivalent to 11.1% of total credit.

Undisbursed loans increased by 15.5% during

the reporting semester. Despite a lower total than

uncommitted UL, committed undisbursed loans grew

more rapidly at 40.9%, compared to just 5.3% for

uncommitted UL, to Rp365.2 trillion. Nevertheless,

uncommitted UL continued to dominate total

undisbursed bank loans with a 65.1% share. The

meteoric rise of undisbursed loans was also in line

with relatively robust credit growth in 2010, reaching

22.8%.

There are also causal relationships between

excess bank liquidity and undisbursed loans. This is

a part of rational business because banks have to

anticipate the withdrawal of committed credit. In

general, banks provide adequate liquidity through Bank

Indonesia Certificates and government bonds (SUN) to

anticipate any withdrawals of committed credit. The

magnitude of this liquidity encourages banks to seek

short-term, low-risk profits, hence the propensity for

short-term investments.

On the other hand, the funds thus available

cannot actually be absorbed by the real sector. Figure

50


2.2.1 shows the trend of total liquid funds9, which

mirror undisbursed bank loans.

private banks, each with a large share of committed

undisbursed loans, are obliged to provide sufficient

liquid funds to cover the possibility of loan withdrawals

by the debtors. This obligation is much less severe for

state-owned banks and joint-venture banks.

Working capital credit dominated undisbursed

bank loans with a 70% share of the total. Furthermore,

uncommitted UL dominated all types of bank loan

(Figure 2.2.3). However, the share of uncommitted

UL for consumption credit exceeded that of all other

types. This is due, among others, to the magnitude

of undisbursed loans for credit cards. Conversely,

uncommitted UL were lowest for investment credit.

Based on economic sector, most undisbursed loans

were for the manufacturing sector, others sector and

trade sector with respective shares of 27.2%, 21% and

20.5%. This is congruous with the credit allocated to

these three sectors, which also dominated. Meanwhile,

all sectors had uncommitted UL in excess of 50%,

while the largest share of committed undisbursed loans

belonged to the trade sector and international business

services (Figure 2.2.4).

1 Liquid funds include Bank Indonesia Certificates, other Bank Indonesia placements, SUN and AFS SUN.

100

150

200

250

300

350

400

450

500

550

600

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

UL Liquid Funds

Committed Uncommitted

11.9%

49.7%66.9%

10.8%

51.0%34.9%

88.1%

50.3%33.1%

89.2%

49.0%65.1%

0%

20%

40%

60%

80%

100%

StateOwned

NationalPrivate

RegionalDevelopment

JointVenture

Foreign Total

committed uncommitted

Based on bank group, uncommitted UL dominated

the undisbursed loans of state-owned banks and

joint-venture banks (Figure 2.2.2). Meanwhile, the

undisbursed loans of regional development banks and

foreign banks were more dominated by committed UL.

Finally, the share of undisbursed loans for private banks

was relatively balanced. Considering that committed UL

cannot be cancelled by the banks and that banks are

committed to liquidating the facility to its customers,

then regional development banks, foreign banks and

34.9%43.8%

30.3%

65.1%56.2%

69.7%

0 %

20%

40%

60%

80%

100%

MSM IC CC





Figure Box 2.2.1Undisbursed Loans and Liquid Funds

Figure Box 2.2.2Share of Undisbursed Loans by Bank Group

Figure Box 2.2.3Share of Undisbursed Loans by Type

51


Looking ahead, undisbursed loans are predicted

to continue increasing in relation to higher inflation

triggered by higher interest rates, weaker public

purchasing power and higher operational costs.

Businesses will become more selective in their use of

credit facilities.0%

25%

50%

75%

100%

Agr

icul

ture

Min

ing

Indu

stry

Elec

tric

ity

Con

stru

ctio

n

Trad

e

Tran

spor

tatio

n

Busin

ess

Serv

ice

Soci

alSe

rvic

e

Oth

ers



Figure Box 2.2.4Share of Undisbursed Loans by Economic Sector

52


Box 2.3 Impact of Credit on Inflation

Banks are one of the institutions that play a role

in nurturing development through intermediation

activities, namely the allocation of credit. Funds

obtained from credit enable agents (households/firms)

to boost their production capacity.

Based on the transmission of interest rates to

inflation, it is widely accepted that interest rates have an

impact on investment and GDP. GDP has a subsequent

affect on output gap, which itself influences inflation

(Mankiw 2005 and Miskhin 2005). By making the

assumption that the interest rate is represented by

credit allocated to firms and households, then credit

based on type will affect the output gap, which can

be illustrated as follows:

The question that emerges is whether current

credit growth could spur inflationary pressures that

exceed the predetermined inflation target? One of the

ways to answer this question is to observe credit from

the perspective of its type of use. Credit can be split

into two broad groups. The first group incorporates

investment credit and working capital credit, which

affects the supply of goods and services. The second

group consists of consumption credit, which affects

the demand for goods and services. The first group

can be analysed based on sector, while the second

type of credit can be analysed by observing its end

use. There are two hypotheses involved, the first being

that languid credit growth to the first group will result

in limited production of goods and services, hence,

reducing supply and raising inflation. Meanwhile, the

other hypothesis states that excessive credit allocation

to the second group will precipitate strong demand

and, consequently, higher inflation.

Figure Box 2.3.1Factors that Influence Inflation

Core=4,18%

53


Accordingly, we can analyse whether credit

growth to the first group is insufficient and whether

credit growth to the second group is too high, thus

driving inflation beyond its predetermined target

corridor. Two approaches can be used to answer

these questions, namely by observing long-term

correlations as well as optimal output growth. The

second approach is the non-accelerating inflation rate

of credit growth.

Research shows that investment credit growth

remains below the optimal rate, while working capital

credit is slightly above its optimal rate but still within

the bounds of optimal. It can, therefore, be concluded

that credit growth to the first group remains within

safe limits. Notwithstanding, research has also shown

that consumption credit growth is above the optimal

desirable range.

Consequently, rapid consumption credit growth

requires closer attention and has the potential to

exacerbate inflation. From the respective composition

of each type of credit against total bank credit,

aggregate credit growth remains within safe limits.

This implies that current credit growth is conducive

to inflation remaining within its predetermined target

corridor.

54


Box 2.4 Prime Lending Rate Policy Transparency

Publications regarding the prime lending rate

have been written by numerous authors. In these

publications, another term for the base lending rate

is the prime rate. Suanders (2003) explained that the

prime rate is the interest rate offered to the lowest

risk customers, while according to Glodberg (1981)

the prime rate is a key indicator used to observe

credit market conditions. Meanwhile, Naber, Park

and Suanders (1993) postulated that the prime rate

can no longer be seen as the lending rate offered to

creditworthy customers but it has developed to become

a key indicator in the calculation structure of lending

rates offered by a particular bank. Based on these

publications, several countries already apply a prime

rate policy, including Malaysia and India.

Referring to existing publications and the

application of prime rate policy in a number of

countries, as well as based on the results of reviews

and discussions with third parties, Bank Indonesia

promulgated a new regulation regarding the transparent

publication of the prime lending rate, effective from

31st March 2011. The regulation is only applicable to

conventional commercial banks. The regulation refers

to Bank Indonesia Regulation (PBI) No. 7/6/PBI/2005

concerning Transparent Banking Product Information

and Personal Customer Data, as well as PBI No. 3/22//

PBI/2001 on Transparent Bank Financial Conditions,

amended by PBI No. 7/50/PBI/2005.

The objective of the new regulation is to: i)

enhance transparency regarding the characteristics

of banking products, including the benefits, costs

and risks in order to provide an explanation to the

customer; and ii) bolster good governance and promote

healthy competition in the banking industry through

the creation of better market discipline.

Basically, the prime lending rate is the lowest

interest rate used by banks to determine the lending

rates offered to the customer. The prime rate is

calculated for the rupiah and three types of loan,

namely corporate credit, retail credit and consumption

credit (mortgages and non-mortgages). Funding from

credit cards and uncollateralised loans is not included

in non-mortgage consumption credit. Classification

of credit type is based on criteria set internally by the

banks.

The prime lending rate is the result of calculating

three components, namely the cost of funds, overhead

costs and profit margin. A risk-premium is then

added for each individual customer depending on

the bank’s evaluation of debtor risk. Consequently,

the lending rate offered to the customer is rarely

the same as the prime rate. In addition, the prime

lending rate is calculated per annum and expressed

as a percentage.

At the preliminary stage, banks that have total

assets in excess of Rp10 trillion on and/or after 28th

February 2011, based on the monthly bank report,

are required to publish their prime lending rate.

Such banks are obligated to publish their prime rates

simultaneously through: i) notice boards in each bank

branch; ii) the main page of the bank’s website, if

the bank maintains a website; and iii) newspapers in

conjunction with the Quarterly Financial Statements

for March, June, September and December. In the

55


event that a bank does not maintain a website, the

prime lending rate need only be published at each

bank branch and in the newspaper along with the

quarterly financial statement. In the case of a bank’s

total assets dropping to below Rp10 trillion, it is still

mandatory for the bank in question to publish its prime

lending rate.

Regarding the reports that must be submitted

to Bank Indonesia, all conventional commercial banks

are obliged to compile a prime rate calculation report

pursuant to prevailing regulations. This report must be

submitted to Bank Indonesia on a quarterly basis along

with the quarterly financial statement.

The imposition of sanctions pertaining to the

publication of prime lending rates refer to PBI No.

7/6/PBI/2005concerning Transparent Banking Product

Information and Personal Customer Data, amended

by PBI No. 7/50/PBI/2005, namely administrative

sanctions consisting of written warnings and financial

penalties.

The regulation for the publication of prime

lending rates is expected in the long term to create

healthy competition among banks and, hence, bring

down lending rates. Ultimately, lower lending rates

will boost public/corporate demand for credit and

catalyse the real sector, thus propping up the national

economy.

56


Box 2.5 Sources of Bank Profitability

Up until December 2010, banks continued to rely

on interest income as their main source of income with

a 71.70% share of operational income in December

2010 (Figure 2.4.1). Nevertheless, the share of interest

income has followed a declining trend since December

2006. The main components of interest income are

credit totalling Rp204.0 trillion and placements in

securities (SSB) amounting to Rp22.53 trillion with

respective shares of 81.10% and 8.88% in December

2010. When compared to operational income, the

share of credit expands to 58.36% and the share of

SSB declines to 6.65%. from SSB was lower in the second semester of 2010

compared to the first at 47.60% of total SSB income.

Therefore, the banks sought alternative sources of

income in order to compensate for the decline in SSB

income, namely fee based income. The share of fee

based income to total operational income increased,

albeit relatively slowly, from 3.77% in December 2000,

equivalent to Rp4.91 trillion, to 9.09% in December

2010 or Rp31.91% (Figure 2.4.2). For 2010, fee based

income in semester-II 20010 exceeded that received

in semester-I, more specifically reaching 54.84%

(Rp17.50 trillion). This is a noteworthy achievement

considering that it reflects banks diversifying their

sources of income. If such conditions persist, interest

from bank credit is expected to decline further as banks

move away from their reliance on income from the

allocation of credit.

By bank group, in December 2010 fee based

income to operational income for foreign banks was

the highest at 12.91% (Rp4.80 trillion) but lower than

the position in December 2009 at 13.56% (Rp4.27

Figure Box 2.5.1Share of main sources of Income to

Operational Income (%)

0

10

20

30

40

50

60

70

80

90

Dec 00 Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10

Interest Income ( ) Credit ( ) SSB ( ) Fee Based (right)

Figure Box 2.5.2Share of Fee Based Income to

Operational Income (%)

0123456789

1011121314

Dec 00 Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10StateOwned Private



Income from the extension of credit has trended

upwards since December 2000, which reflects the

reliance of banks on credit as a main source of interest

income. In 2010, more than half of the total Rp204.0

trillion of credit interest received was during the second

semester (51.71%). Meanwhile, the share of income

from SSB to operational income declined from 33.93%

(December 2001) to 6.65% (December 2010). Income

57


trillion). Nominally, however, state-owned banks

received the most fee based income of all bank groups

(Rp13.08 trillion). Such conditions reflect that foreign

banks are capable of securing alternative sources

of income, excluding credit. In terms of trends, the

share of fee based income for foreign banks was very

fluctuative, while for state-owned banks the share has

increased steadily from 2.41% (December 2000) to

10.97% (December 2010).

Since December 2002, the share of fee based

income to operational income for regional development

banks has remained the lowest of all bank groups. In

December 2010, the share of fee based income for

development banks posted 3.16% growth, which

was lower than that posted in December 2009 at

5.44%. Meanwhile, the share of fee based income

for private banks has declined since December 2008

and, conversely, that for joint venture banks has

increased.

The deluge of foreign capital inflows in 2010,

among others, was reflected by an increase in foreign

currency/derivative transactions by banks. Profits

from such transactions reached Rp48.30 trillion in

December 2010 (primarily stemming from spot and

swap transactions) surpassing that posted in December

2009 at Rp24.39 trillion (Figure 2.5.3). However,

banks also simultaneously incurred losses from such

transactions totalling Rp44.61 trillion (principally spot

and swap transactions) in December 2010, up from

Rp18.23 trillion in December 2009.

-

10

20

30

40

50

60

Dec 00 Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10

Loss


Figure Box 2.5.3Bank Foreign Currency/Derivative Transactions

(trillions of rupiah)

59

Chapter 3. Strengthening Financial Infrastructure

Chapter 3Strengthening FinancialInfrastructure

60



61


Chapter 3 Strengthening Financial Infrastructure

The payment system was free from disruptions and remained secure during the

reporting period. A number of measures were taken by Bank Indonesia during

semester-II as part of the strategy to bolster monetary management as well as

preserve financial system stability, and also in response to the deluge of foreign

capital inflows amid a shallow domestic money market and limited economic

absorption capacity. Against this backdrop, financial sector liberalisation in

the ASEAN region and global financial reforms became the principal agenda

of international fora, which were expected to provide huge benefits to the

national economy. In this context, closer coordination among the financial

authorities, including Bank Indonesia, the Ministry of Finance and the Deposit

Insurance Corporation is required to reinforce financial system stability.

3.1. PAYMENT SYSTEM EFFICIENCY

3.1.1. Payment System Performance

Nominally, BI-RTGS (Real-Time Gross Settlement)

transactions dominated non-cash payment system

transactions with a 93.1% share. In terms of transaction

volume (frequency),card-based payment instruments

consisting of credit cards, ATM cards and ATM/credit cards

provided the largest contribution with a 95% share of all

transaction volume in the non-cash payment system.

Figure 3.1Nominal Transactions (billions of rupiah)

86

88

90

92

94

96

98

100

Jan

09Fe

b 09

Mar

09

Apr

09

May

09

Jun

09Ju

l 09

Aug

09

Sept

09

Oct

09

Nov

09

Dec

09

Jan

10Fe

b 10

Mar

10

Apr

10

May

10

Jun

10Ju

l 10

Aug

10

Sept

10

Oct

10

Nov

10

Dec

10

%

RTGS NCS CBP

- RTGS : Real-Time Gross Settlement - NCS : National Clearing System - CBP : Card Based Payment

Source: Enterprise Data Warehouse, Bank Indonesia

62


3.1.1.1. BI-RTGS System

3.1.1.1.1. Transactions

The average daily value and volume of transactions

in the BI-RTGS system during semester-II 2010 was Rp228

trillion and 59,828 transactions respectively with a total

value and volume of Rp28.5 thousand trillion and 7.4

million transactions in the reporting semester. When

compared to the same period of the previous year (yoy), the

value and volume increased correspondingly by 32% and

23.2%. The growth in transaction value was primarily due

to an expansion in the value of government transactions

and monetary management transactions amounting to

51% and 33% respectively.

10 An indicator that illustrates liquidity tightness at the systemic level. This indicator has a range of 0 – 1, where a value approaching 1 indicates tighter liquidity in the system.

11 The turnover ratio is a comparison between outgoing transactions settled through the bank’s account available at the beginning of the day. A high turnover ratio denotes that a bank favours servicing its liabilities by waiting for incoming transfers from other banks in contrast to utilizing its own capital, hence, transactional behaviour is reflected by a higher turnover ratio.

Figure 3.2Transaction Volume (in thousands)

0

10

20

30

40

50

60

70

80

90

100

Jan

09Fe

b 09

Mar

09

Apr

09

May

09

Jun

09Ju

l 09

Aug

09

Sep

09O

ct 0

9N

ov 0

9D

ec 0

9Ja

n 10

Feb

10M

ar 1

0A

pr 1

0M

ay 1

0Ju

n 10

Jul 1

0A

ug 1

0Se

p 10

Oct

10

Nov

10

Dec

10

RTGS NCS CBP

%

- RTGS : Real-Time Gross Settlement - NCS : National Clearing System - CBP : Card Based Payment

Figure 3.3BI-RTGS System Transactions

3.1.1.1.2. Operational Activity and Liquidity

Management

Taken holistically, BI-RTGS system performance

during semester-II 2010 was good. Similar to the previous

semester, disruptions to the communication network were

the most common problem faced with application and

hardware issues also reported. Nonetheless, operational

risk attributable to such disruptions was successfully

mitigated through the Business Continuity Plan (BCP) and

Disaster Recovery Plan (DRP).

BI-RTGS system liquidity was sufficiently loose during

the second semester of 2010, as reflected by the liquidity

usage indicator10 and turnover ratio11 . The liquidity usage

indicator was in the range of 30%-35% in the second

semester of 2010, which signifies sufficiently loose liquidity

in the BI-RTGA system to efficiently settle transactions.

Relatively loose liquidity was further evidenced by an

average turnover ratio of 1.4 in semester-II 2010.

From a total of 191 participants in the BI-RTGS

system, only one bank utilised the intraday liquidity facility

(ILF). The provision of an intraday liquidity facility by Bank

Indonesia helped mitigate risk by overcoming temporary

funding shortfalls faced by a participating bank that occur

during regular operating hours in order to avoid gridlock.

The relatively limited use of the liquidity facility during

semester-II is further verification of adequate liquidity in

the BI-RTGS system, which represents the banks’ checking

accounts.

0

200

400

600

800

1,000

1,200

1,400

1,600

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Jan 2

009

Feb 2

009

Mar

c 2009

Apr

2009

May

2009

June

2009

July

2009

Aug 2

009

Sept

2009

Oct

2009

Nov

2009

Dec

2009

Jan 2

010

Feb 2

010

Mar

c 2010

Apr

2010

May

2010

June

2010

July

2010

Aug 2

010

Sept

2010

Oct

2010

Nov

2010

Dec

2010

VolumeValue

Value (trillion Rp) Volume (thousand)



63


3.1.1.2. Bank Indonesia Scripless Securities

Settlement System (BI-SSSS)


The average daily value and volume of BI-SSSS

transactions in semester-II 2010 was Rp58.5 trillion and

435 transactions respectively, bringing the total for the

semester to Rp7.2 trillion and 53.9 thousand transactions.

Compared to the same period in the previous year (yoy)

transaction value and volume experienced respective

growth of 35.5% and 0.7%.

Figure 3.4BI-SSSS Transactions

3.1.1.2.2. Administration of Securities

The average daily total and value of securities

processed through the Bank Indonesia-scripless securities

settlement system up to the end of semester-II 2010 was

154 securities with a total value of Rp1,022 trillion, which

can be broken down as follows:

A significant increase was reported for the total and

value of term deposits in semester-II 2010, attributable

to Bank Indonesia’s policy of issuing term deposits with a

minimum 3-month tenor to prevent the sudden withdrawal

of capital inflows, particularly foreign capital. Meanwhile,

the decline in total and value of Bank Indonesia Certificates

(SBI) and Bank Indonesia Sharia Certificates was due to

the policy of Bank Indonesia to hold SBI auctions every six

months instead of every three months.

3.1.1.3. National Clearing System


The average daily value and volume of transactions

in the national clearing system was Rp7.3 trillion and

381 thousand transactions respectively in the second

semester of 2010, making the total for the semester

Rp917 trillion and 47.7 million transactions. Compared

Type of SecurityTotal Total TotalValue Value Value

Semester I 2010 Semester II 2010

Deposit Facility 2 23,455,260,162,602 2 31,453,320,000,000 0% 34%

Sharia Deposit Facility 2 3,057,960,975,610 2 4,747,485,600,000 0% 55%

Term Deposit 10 31,432,677,731,707 46 99,439,784,672,000 339% 216%

Ijarah 12 25,603,734,390,244 14 36,452,693,920,000 19% 42%

Treasury Bonds 63 555,245,955,479,675 62 583,967,729,216,000 0% 5%

Bank Indonesia Certificates 40 298,659,633,886,179 16 235,548,512,208,000 -60% -21%

Bank Indonesia Sharia Certificates 4 2,789,800,000,000 2 1,436,296,000,000 -40% -49%

Treasury Bills 9 24,120,406,504,065 9 29,622,200,000,000 3% 23%

142 964,365,429,130,081 154 1,022,668,021,616,000 9% 6%

Table 3.1BI-SSSS Transactions


Source: Bank Indonesia Scripless Securities Settlement System

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

-

500

1,000

1,500

2,000

2,500

Jan

2009

Feb

2009

Mar

2009

Apr2

009

May

2009

Jun

2009

Jul2

009

Aug

2009

Sep

2009

Oct

2009

Nov2

009

Dec

2009

Jan

2010

Feb

2010

Mar

2010

Apr2

010

May

2010

Jun

2010

Jul2

010

Aug

2010

Sept

2010

Oct

2010

Nov2

010

Dec

2010

Value Volume

Value (Trillion Rp) Volume

64


to the same period in the previous year (yoy) transaction

value and volume experienced respective growth of 13%

and 12.7%.

3.1.1.5 Credit Card Industry

The value and volume of transactions initiated by

credit cards was Rp86 trillion and 103 million transactions

in semester-II 2010. The value and volume increased by

17.6% and 8.6% respectively when compared to the

same period in the previous year (yoy). Positive credit card

transaction growth in semester-II 2010 was primarily due

to aggressive promotion by card issuers, which ultimately

boosted seasonal transactions at the end of the year.

Figure 3.5National Clearing System Transactions

Figure 3.6ATM/Debit Card Transactions

Figure 3.7Credit Card Transactions

3.1.1.3.2. Operational Activities and Liquidity

Management

Similar to BI-RTGS, the national clearing system, in

general, performed well in the second semester of 2010,

which was a continuation of the sound performance

reported in the previous semester and confirmed adequate

liquidity in the system. This was further corroborated by

the availability of a prefund (cash and collateral) provided

by participating banks as a daily prerequisite to clearing

participation.

3.1.1.4. ATM and ATM/Debit Card Industry

The value and volume of transactions initiated by

ATM cards as well as ATM/debit cards reached Rp1,061

trillion and 932 million transactions in semester-II 2010.

When compared to the same period in the previous year

(yoy), the value and volume increased by 17.3% and

13.1% respectively.

0

1,0002,000

3,0004,000

5,0006,000

7,000

8,0009,00010,000

0

20

40

60

80

100

120

140

160

180

Jan

2009

Feb

2009

Mar

2009

Apr2

009

May

2009

Jun

2009

Jul2

009

Aug

2009

Sept

2009

Oct

2009

Nov

2009

Dec

2009

Jan

2010

Feb

2010

Mar

2010

Apr2

010

May

2010

Jun

2010

Jul2

010

Aug

2010

Sept

2010

Oct

2010

Nov

2010

Dec

2010

VolumeValue

Value (BillionRp) Volume (Thousand)




-

20

40

6080100

120

140

160

180

-

204060

80100120

140160180

200

Jan

2009

Feb

2009

Mar

2009

Apr

2009

May

2009

Jun

2009

Jul2

009

Aug

2009

Sep

2009

Oct

2009

Nov

2009

Dec

2009

Jan

2010

Feb

2010

Mar

2010

Apr

2010

May

2010

Jun

2010

Jul2

010

Aug

2010

Sep

2010

Oct

2010

Nov

2010

Dec

2010

VolumeValue

Value (Trillion Rp) Volume (Million Rp)

-2468

1012141618

20

-

2

4

6

8

10

12

14

16

18

Jan

2009

Feb

2009

Mar

2009

Apr

2009

May

2009

Jun

2009

Jul2

009

Aug

2009

Sep

2009

Oct

2009

Nov

2009

Dec

2009

Jan

2010

Feb

2010

Mar

2010

Apr

2010

May

2010

Jun

2010

Jul2

010

Aug

2010

Sep

2010

Oct

2010

Nov

2010

Dec

2010

VolumeValue

Value (Trillion Rp) Volume (Million Rp)

65


3.1.1.6 Electronic Money Transactions

The value and volume of transactions utilising

electronic money (e-money) reached Rp354.9 billion

and 14.1 million transactions in semester-II 2010. When

compared to the same period of the previous year (yoy),

the value and volume increased by 7.7% and 43.3%

respectively. Similar to the previous period, robust e-money

growth was buoyed by aggressive promotion by e-money

issuers.

BI-SSSS systems. The following strategic issues motivate

the development of these systems:

a. A need to enhance the reliability (including security

aspects) of infrastructure in terms of operating the

BI-RTGS and BI-SSSS systems to accommodate a

further increase in transaction volume referring

to the Core Principles for Systemically Important

Payment Systems (BIS - CP SIPS) and International

Organization of Securities Commissions (IOSCO)

Recommendations.

b. The need for interoperability with other systems in

the financial system/market, both domestically and

cross-border financial market infrastructure, in line

with an increase in cross-border transactions as well

as to anticipate regional cooperation initiatives, in

particular the formation of the ASEAN Economic

Community in 2015.

c. The need for an efficient settlement mechanism in

the BI-RTGS system, namely by changing the current

gross-based system into a hybrid system that uses

offsetting or netting. This aims to optimise the use of

liquidity and anticipate the requirement for economic

development and financial market deepening.

d. The need for a regulator/authority to enhance the

monitoring efficacy of the financial system/market.

Development of second-generation BI-RTGS and

BI-SSSS systems will enter the design specification and

development phase in 2011 accompanied by module

testing.

In terms of the national clearing system operated

by Bank Indonesia, additional credit clearing settlement

cycles will be incorporated into the BI-RTGS system in 2011

with the development of a direct debit module. The daily

addition of two extra credit clearing settlement cycles, from

two to four, will enhance efficiency from the perspective

of time, thereby bringing credit transfers between banks

close to real time. Meanwhile, development of a direct

Figure 3.8E-money Transactions

3.2. PAYMENT SYSTEM PERFORMANCE AND

RISK MITIGATION

A focal point of Bank Indonesia’s payment

system policy was the development of payment system

infrastructure in preparation for the era of global economic

integration, in particular the planned establishment of the

ASEAN Economic Community (AEC) in 2015. Preparations

were made with due consideration to the performance of

payment system transactions, which have increased year on

year in accordance with economic growth. Furthermore,

Bank Indonesia also needed to anticipate the emergence

of new financial instruments and products as well as the

development of information technology, which will create

financial system integration among countries.

Relating to the large-value payment system, Bank

Indonesia will develop second-generation BI-RTGS and


500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

- -

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Jan 09

Feb 0

9M

ar 09

Apr 0

9M

ay 09

Jun 0

9Ju

l 09

Aug 0

9Se

p 09

Oct 0

9No

v 09

Dec 0

9Jan

2010

Feb 2

010

Mar

2010

Apr 2

010

May

2010

Jun 2

010

Jul 2

010

Aug 2

010

Sep 2

010

Oct 2

010

Nov 2

010

Dec 2

010

VolumeValue

Value (Million Rp) Volume

66


debit module will ameliorate routine payment efficiency

between banks initiated by customers, for instance the

payment of utility bills (electricity, telephone, water, etc).

Policy concerning financial system instruments will

continue to focus on the implementation of standardised

chip technology on ATM/debit cards, development of

standardised electronic money, and initiation of the

National Payment Gateway (NPG).

The roll out of credit cards based on chip technology

began in January 2010 and has successfully mitigated credit

card fraud. Therefore, the expeditious implementation of

chip technology for ATM/debit cards is desirable. Chip

technology implementation in 2011 will focus on the

establishment of a Key Management Institution and

Certification Body. The formation of these two agencies

is a prerequisite to the introduction of chip-based ATM/

debit cards.

To promote the use of electronic money (e-money),

issuers should be encouraged to ensure interoperability

among the systems they develop. A precondition of

interoperability is standardised e-money accepted industry

wide. Bank Indonesia will redouble efforts to develop a

standard form of e-money by facilitating the industry

through a Task Force.

In addition to policies concerning instruments,

Bank Indonesia is also concerned with the infrastructural

efficiency of the retail payment system currently in place

through the development of a National Payment Gateway

(NPG), which is expected to become a single hub for retail

transactions between banks by incorporating a variety of

front-end delivery channels.

Excluding the dimension of payment system policy

already mentioned, Bank Indonesia also strives to expand

the active role played by the payment system industry in

jointly creating with Bank Indonesia a secure and efficient

payment system. In terms of institutionally reinforcing the

payment system industry, Bank Indonesia has facilitated all

components of the payment system industry to form the

Payment System Association of Indonesia (ASPI) to function

as a Self-Regulatory Organisation for micro and technical

issues. ASPI was officially inaugurated on 11th November

2010. Looking ahead, ASPI is expected to become a

strategic partner of Bank Indonesia to foster the creation

and maintenance of a more efficient and reliable payment

system that adheres to consumer protection principles.

67

Chapter 4. Special Topic

Chapter 4Special Topic

68



69


Chapter 4 Special Topic

Bank Indonesia undertook a number of endeavours during the reporting period

as part of its strategy to reinforce financial system stability management, and

also in response to the influx of foreign capital flows amid a shallow domestic

money market as well as limited economic absorption capacity. Accordingly,

financial sector liberalisation in the ASEAN region and global financial reforms

were the main agenda items at international fora, which is expected to

provide huge benefits to the national economy. Closer coordination among

the financial authorities like Bank Indonesia, the Ministry of Finance and the

Deposit Insurance Corporation is required in order to bolster financial system

stability.

4.1. FINANCIAL SECTOR REFORM

The recent crisis spawned the birth of a global

financial sector reform agenda supported by members

of G-20. The reforms promote the creation of global

financial system stability, among others, by enhancing the

bank liquidity and capital regime, strengthening systemic

financial institutions, regulating the OTC derivative

markets, regulating shadow banking and improving

macroprudential policy. Such a comprehensive agenda

is expected to ensure the global financial system is more

resilient and catalyse growth in the real sector.

The global financial crisis in 2008 exposed the need

to reform all lines in the financial sector in terms of both

regulation and supervision. The current banking regulation

regime is considered to have a number of weaknesses as

follows:

economic cycle. Capital and provisioning tend

to remain relatively low while the economy is

stable. Conversely, both are mandatorily raised

(through regulations) when economic conditions

deteriorate.

procyclicality. Oppositely, excessive credit growth

often accompanies robust economic expansion.

II regulatory framework, including that to mitigate

counterparty credit risk and liquidity.

rating agencies. It is clear that credit rating agencies

suffer from conflicts of interest.

On the other hand, bank leverage is too high,

the quality and quantity of capital is inadequate and

an insufficient liquidity buffer was seen as the primary

contributing factor of the recent global financial crisis.

These weaknesses are exacerbated by the problems

70


of procyclicality and also the interconnectedness of

system institutions. Weak risk management, corporate

governance, transparency and supervision were also

highlighted as factors that helped trigger the global

financial crisis.

Consequently, at the G-20 leaders summit in

November 2010, G-20 leaders endorsed a variety of

proposals as part of a financial sector reform package.

At the summit, leaders also voiced their commitment to

adopt an array of financial sector reform packages within

the agreed upon timeframe. It was further agreed that the

financial sector reform packages would spearhead a new

financial sector supervisory and regulatory regime, expected

to be more prudent as well as capable of overcoming

the weaknesses exposed in the previous regime that

contributed to the adverse impacts of the global financial

crisis. Not only were microprudential aspects targeted,

the reforms also touch upon macroprudential elements.

The recent global financial crisis provided a number of

important lessons regarding the importance of adequate

power and tools as well as jurisdiction to overcome

systemic or system-wide risk.

In broad terms the global financial sector reform

agenda covers the following scope:

1. Strengthening the global capital regime and bank

liquidity standards as well as mitigating prevalent

procyclicality, known as Basel III (building high quality

capital and liquidity standards).

2. Regulating systemically important financial

institutions (Addressing systemically important

financial institutions and cross-border resolutions).

3. Reforming the compensation scheme for executives

at financial institutions (reforming compensation

practices).

4. Strengthening OTC derivative market regulations

(improving over-the-counter derivative markets).

5. Strengthening adherence to international

standards.

6. Strengthening accounting standards.

7. Developing macroprudential policy frameworks and

tools.

8. Harmonising market and financial institution

regulations (differentiated nature and scope of

regulation).

9. Hedge fund regulations.

10. Regulating credit rating agencies.

11. Establishing supervisory colleges.

12. Reactivating securitisation based on a strong

prudential foundation (re-launching securitisation

on a sound basis).

4.1.1. Basel III

Of all the agenda items mentioned, Basel III is the

primary focus considering that it covers a range of macro

and microprudential regulatory aspects. This is reasonable

bearing in mind that the majority of the economy is bank

based. One of the most important components of Basel

III is the capital requirement, more specifically minimum

common equity of 4.5% of risk-weighted assets (ATMR)

as well as raising the capital adequacy ratio from 8.0% to

10.5% after calculating the conversion buffer of 2.5%.

In addition, two new bank liquidity regulations have been

determined, namely the liquidity coverage ratio (LCR) and

the net stable funding ratio (NSFR).

The birth of Basel III is inextricable from the desire

to create a more resilient banking sector with enhanced

absorption capacity. This is viewed as critical momentum

for the emergence of a new capital and liquidity regime that

is more prudent and synergises not only microprudential

aspects and macroprudential aspects too. The recent

global financial crisis proved that the resilience of individual

institutions was not enough to minimise the impact of the

crisis because the interconnectedness between financial

institutions was not merely at the domestic level but also

71


globally, therefore, compounding the possibility of systemic

risk in the event of one financial institution failing.

Microprudentially, the Basel III framework attempts

to overcome the panoply of weaknesses identified in the

previous capital framework, numerator and dominator,

concerning the calculation of capital adequacy required

by a bank. Basel III defines a higher level and quality of

capital with a focus on common equity. Furthermore,

Basel III also increases the scope of risk, particularly that

linked to activities on the capital market like trading book,

securitisation, as well as counterparty credit risk on OTC

derivatives and repos, namely by not only applying a higher

risk weighting but also by applying a higher capital charge

on certain activities.

In addition, Basel III strives to focus on the importance

of providing an adequate capital buffer by individual

financial institutions, namely by requiring a conservation

buffer. The concept of a conservation buffer is expected

to precipitate a change in the distribution policy paradigm

(and incentive policy) in order to ensure banks maintain

sufficient sources of conservation capital and avoid undue

distribution (pay dividends, bonuses, etc).

Meanwhile, other elements of Basel III, for instance

the concepts of leverage ratio, countercyclical capital buffer

and capital surcharge for systemically important financial

institutions, are aimed at improving macroprudential

aspects. Macroprudential is an issue that is still being

discussed at international fora. Macroprudential policy is

considered complementary to microprudential policy and

also interactive with other policies that impact on financial

stability. While this is not a new issue, it does however

attract a variety of definitions by different authorities in

different jurisdictions because although a wide range of

polices actually affect financial stability and systemic risk,

not all of them are considered macroprudential.

The salient features of Basel III are as follows and

subsequently presented in Table 4.1:

1. Enhance the quality of Tier 1 capital. One way is

through the requirement for predominant common

equity on Tier 1 capital, simplification of Tier 2

capital as well as abolishing Tier 3 capital and Tier 1

innovative capital.

2. Mitigate procyclicality through the countercyclical

capital framework that proposes the application of

forward-looking provisioning, a capital conservation

buffer and a countercyclical capital buffer.

3. Apply a leverage ratio as a measure to limit leverage

in the banking sector.

4. Improve the capital requirements for exposure to

counterparty credit risk (CCR).

5. Apply global liquidity standards that legislate the

application of two new liquidity standards, namely

the liquidity coverage ratio (to monitor short-term

liquidity resilience) and the net stable funding ratio

(to monitor long-term liquidity resilience) as well as

propose the application of four liquidity monitoring

tools.

in particular tighter requirements for capital and

risk weighting on trading book, derivative and

securitisation transactions.

The Basel III framework, which synergises micro

and macroprudential aspects, will help bolster banking

industry resilience in Indonesia due to stronger motivation

to monitor macroprudential aspects in order to more

accurately identify potential systemic risk stemming from

a specific activity or bank exposure as well as market

infrastructure. In addition, coupled with an effective

resolution regime framework, which is the objective of

regime reform, it is expected to nurture a prudent and

responsible banking industry in terms of risk management,

as well as create an independent banking sector that can

resolve its own problems without relying on injections from

the public sector to ensure business continuity.

72


Leaders Summit in Seoul in November 2010, commitment

to the application of the reform agenda for the financial

sector has been forthcoming, including Basel III with an

agreed timeframe by all member states. In general, the

phase-in arrangement for full Basel III implementation

leads up to yearend 2018, with fully effective application

commencing on 1st January 2019.

The transition period for the application of the

leverage ratio begins on 1st January 2011, during which

time BCBS will assist semi-annually in order to evaluate

whether the application of 3% minimum leverage Tier

1 is sufficient for all credit cycles and business models,

the scope of exposure used in the ratio is adequate, and

to ensure the accountancy standards in effect in each

respective jurisdiction do not impact the leverage ratio.

To determine the impact of Basel III implementation

on bank capital, Bank Indonesia organised a quantitative

impact study (QIS) together with 14 large banks based on

December 2009 data. In general, the results demonstrated

that the application of Basel III will not have a significant

impact on bank capital in Indonesia considering that

the majority of bank capital is common equity, the

leverage ratio is above the minimum requirement and

Table 4.1Salient Regulations and Implementation Schedule of Basel III

No Agreement Ratio Implementation

1. Common equity. 4.5% Gradual, commencing in 2013 at 3.5% with full implementation of 4.5% in 2015.

2. Tier 1. 6%. Transition begins in 2013 at 4.5% with full implementation of 6% in 2015.

3. Total capital excluding conservation buffer. 8%.

4. Conservation buffer 2.5%. Transition begins in 2016 at 0.625% with (comprised of common equity). full implementation of 2.5% commencing 1st January 2019.

5. Countercyclical capital buffer (no further 0-2.5%. Applied in the event of excessive credit guidelines, comprised of common equity). growth in a jurisdiction.

6. Minimum total capital plus conservation buffer. 10.5%. Commencing in 2013 at 8% with full implementation of 10.5% effective from 1st January 2019.

7. Leverage ratio (capital exposure/exposure 3%. Supervisory monitoring (2011-2012), measure). parallel run of 3% (1 Jan 2013 – 1 Jan 2014), disclosure begins 1st January 2015, final adjustment to H1-2017, migration to

8. Liquidity coverage ratio (stock of high-quality Min 100%. Observation 2011-2014, regulatory liquid assets/total net cash outflows). reporting commencing in 2012, application of minimum standard (currently under revision) on 1st January 2015.

9. Net stable funding ratio (available stable Min 100%. Observation commencing in 2012-2017, funding/required stable funding). regulatory reporting begins 1st January 2012, application of minimum standard (currently under revision) on 1st January 2018.

on 1st January 2019 (100%).

qualify as non-core Tier 1 and Tier 2.

73


the conservation buffer is adequate. Average bank

capital adequacy ratio (CAR) is currently 13.84%, which

is well above the minimum requirement stipulated in

Basel III at 8% or 10.5% if the conservation buffer is

included. Therefore, bank capital would only experience

a slight decline (1.49%), namely from 15.33% before the

application of Basel III to 13.84%.

Under a framework of monitoring the level of bank

capital during the transition period commencing in 2011,

Bank Indonesia calculates the level of bank capital in

line with the concepts of Basel III using a banking data

approach contained in the commercial bank reports based

on data from December 2010. In general, the average

capital adequacy ratio for banks was 14.82%, which

exceeds that recorded in QIS 2009. The levels of common

equity and Tier 1 were 14.03% and 14.97% respectively,

which meets the BCBS requirements of 4.5% and 6%. The

average capital of respondents was in excess of 8% and

even surpassed 10.5%, which includes the capital buffer.

The results of monitoring the leverage ratio indicate that

banks in Indonesia are not overleveraged with an average

leverage ratio of 8.08%, which is well in excess of the BCBS

minimum requirement of 3% (full results are presented

in Table 4.2).

Table 4.2Recapitulation of Capital Ratio and Leverage Ratio

QIS (Dec 2009) Monitoring *) (Dec 2010)

Value ValueGroup 1**) Group 2)

Respondent

Ratio

Respondent

Ratio

BCBS

Requirements

QIS BIS Result Dec’09

After Basel III

Total Capital 157.469.108 15,33% 207.450.027 15,43% Tier 1 net 134.777.598 13,12% 179.334.640 13,34% 10,50% 9,80%

Tier 2 28.137.416 2,74% 28.115.388 2,09% Risk-Weighted Assets 1.026.926.623 1.344.105.374

Before Basel III

Total Capital 142.164.099 13,84% 199.262.626 14,82% 8,00% Tier 1 net 118.177.024 11,51% 188.519.981 14,03% 6,00% 6,30% 8,10%

Common Equity Tier 1 92.767.269 9,03% 201.202.291 14,97% 4,50% 11.1% 10,70% Additional Tier 1 9.590.209 0,93% - Tier 2 23.987.075 2,34% 10.742.645 0,80%

Changes to Tier 1 (16.600.573) -1,62% 9.185.341 0,68% Changes to Tier 2 (4.150.341) -0,40% (17.372.742) -1,29%

Capital Changes (15.305.009) -1,49% (8.187.401) -0,61% Leverage Ratio 8,21% 8,08% 3,00% 2,80% 3,80% Capital Exposure 118.177.024 188.519.981 Exposure Measure 1.438.594.061 2.331.927.324

Conservation Buffer 82,15% 2,50% 76,00%

Note: *) Source: Monthly Commercial Bank Report **) Banks with total assets of $3 billion, internationally active bank, diversified bank

74


4.1.2. Macro-Microprudential Policy and the

Financial Institution Resolution Regime

Consensus has been reached among all other macro

and microprudential policies. In terms of microprudential

policies, supervision and the resolution mechanism of

systemically important financial institutions (SIFI) will

be ameliorated. In addition, the macroprudential policy

framework will be strengthened in order to mitigate

system-wide risk. Subsequently, a number of fundamental

policies concerning the international agenda have been

accomplished.

FSB, BIS and the IMF were mandated by the G-20

to compile a macroprudential policy framework, including

tools, and define macroprudential policy as a policy that

utilises prudential tools designed to limit systemic risk or

system-wide financial risk, thereby, limiting disruptions to

key financial services that can undermine the real economy.

In other words, there are three aspects in the definition

of macroprudential policy, namely that which is based

on objective aspects, the scope of analysis as well as the

instruments and governance of the policy.

Based on the results of stock-taking macroprudential

policy as well as case studies conducted by the Committee

on the Global Financial System (CGFS), CGFS compiled seven

general macroprudential policy principles that include: i)

integrating various information to diagnose systemic risk;

ii) monitoring and understanding interlinkage between

financial institutions and the market, including cross-border

and hedging market exposure; iii) preparing instruments

and financial infrastructure policy according to specific

risks or diagnoses of imbalances; iv) sharing information at

the international level as an incentive; v) macroprudential

policy is the responsibility of an independent central agency

or formal committee arrangement; vi) explanation of the

mandate, objectives, authority and accountability of the

macroprudential authority; and vii) the communication

strategy for macroprudential policy.

Connected to the macroprudential policy framework,

non-prudential instruments can be considered as

macroprudential policy tools as long as they are targeted

explicitly and specifically to mitigate systemic risk and are

supported by an appropriate governance arrangement.

For example, there is the practice in a number of countries

to view the application of monetary policy as a tool to

limit credit expansion, representing a macroprudential

instrument. Meanwhile, at the implementation level to

enhance system-wide oversight, several jurisdictions have

introduced legislative reforms. The Financial Stability

Oversight Council (FSOC) was established in the United

States, while in Europe the European Systemic Risk Board

(ESRB) was set up. The reforms aimed to focus regulations

on macroprudential policy,

Another issue that is currently the focus of discussion

at international fora and which strives to overcome

systemic risk is a cross-border resolution regime. In this

context, draft key attributes are being drawn up, which

will be adopted by the national resolution regime. The key

attributes of an effective resolution regime are expected to

provide a comprehensive framework (including for NFBI)

to be used by jurisdictions to build, reform and realise

a consistent resolution regime across jurisdictions and

allow the implementation of an effective cross-border SIFI

resolution. The resolve of financial institutions during the

crisis and the power of authorities to introduce structured

resolutions to systemic situations were invaluable lessons

that nurtured the idea of reforming the cross-border

resolution regime to be more effective and ensure that

taxpayers do not bear the losses incurred by a crisis.

One resolution proposed in discussions regarding

resolution regimes was bail-in, seen as having the potential

to be sensitive for financial stability because it requires

a change to the legal framework. Despite its positive

objective, namely to avoid taxpayer exposure to losses

incurred by the failure of a financial institution, bail-in is

75


sensitive for investors in capital instruments. Furthermore,

the bail-in framework that incorporates statutory bail-in

requirements, which demands cross-border resolution

regime harmonisation, will face difficulties in a number

of jurisdictions due to the required change in the legal

framework. The proposed resolution regime is also linked

to the Basel III capital framework, for instance between

the resolution order in the regime framework and the

requirement for a point of non-viability as well as the

surcharge on SIFIs. The finalisation of the resolution regime

framework will determine the overall capital framework,

like the treatment of contingent capital to meet the

systemic surcharge and the possibility of using it to meet

the buffer requirement.

4.1.3. Consumer protection

The financial sector reform agenda does not

stop with improvements from an industry perspective,

consumer protection and the concerns of emerging market

countries are also issues that require the attention of

G-20 leaders in 2011. FSB in conjunction with OECD and

other international organisations were requested by G-20

to explore other options to enhance consumer finance

protection. In addition, FSB along with the IMF and World

Bank were called upon to review the issues of financial

stability that are a concern for emerging market countries

as well as developing countries, and subsequently submit

policy recommendations, for example related to exchange

rate risk management by financial institutions, firms and

households; the regulatory and supervisory capacity of

emerging market and developing countries; issues with

foreign banks and deposit guarantees, financial inclusion,

information sharing among host and home countries, and

trade finance.

4.1.4. Indonesia’s Position on Global Financial

Sector Reform

Excluding the agenda for financial sector reform

agreed by G-20 leaders in November 2010, a critical point

is currently to ensure the implementation of financial sector

reform at the national level, which is consistently globally,

in order to avoid regulatory arbitrage through inconsistent

implementation and shadow banking. The long phase-in

arrangement is thought to provide the relevant authorities

with enough time to meet the requirements of the new

regulatory and supervisory framework.

From Indonesia’s standpoint as an emerging market

country and a member of the G-20, implementation of

financial sector reforms domestically will substantiate

Indonesia’s commitment to international fora. Ideally, the

adoption of a capital framework by enhancing the quality

and level of bank capital will have a positive impact on the

risk absorption capacity of the banking industry in Indonesia.

The inability to accurately predict the contributing factors

of future crises necessitates the banking industry to be

more prepared and, hence, maintain an adequate buffer

to confront the possibility of stress in the future.Similarly,

the liquidity buffer requirements demanded by Basel III

are expected to bolster liquidity resilience in the banking

industry in Indonesia, particularly during a crisis episode.

Furthermore, despite a moderate bank leverage level in

Indonesia, the business model of domestic banks remains

traditional, therefore, the application of the leverage ratio

will help limit the possibility of a high leverage level (as

a backstop) as the business model in Indonesia develops.

76


4.2. FINANCIAL SYSTEM SAFETY NET:

COOPERATION TO CREATE AND MAINTAIN

FINANCIAL SYSTEM STABILITY

A financial system safety net is a vital part of

infrastructure in the financial system. Due to its critical

role, the safety net mechanism is an inseparable part of the

financial system in many countries. The crisis experience

provided an important lesson that safety nets, incorporating

liquidity assistance to financial institutions that experience

liquidity shortfalls and cooperation between authorities/

institutions in the financial sector, contribute greatly to

shielding the economy from the most severe crisis impacts.

Accordingly, the safety net mechanism, which consists of

preventing and resolving crises in the financial sector, must

be well formulated and strengthened.

Underpinning a financial system safety net is

supported through coordination among the authorities/

institutions that play a role in creating and maintaining a

stable financial system. In this context, cooperation and

coordination between Bank Indonesia, the Ministry of

decisive. Realising the importance of such cooperation

and coordination, Bank Indonesia, the Ministry of Finance

and the Deposit Insurance Corporation have signed a

number of agreements in the financial sector as evidence

of commitment and active involvement in efforts to create

and maintain financial system stability including, among

others, an agreement to bridge the implementation of a

financial system safety net mechanism.

is implemented through joint decrees, namely SKB No.

in October 2009. At their core these joint decrees control

the coordination and exchange of data and information

under a framework of supporting the effective task

These joint decrees were enacted in order to enhance and

refine existing coordination between the two organisations.

Coordination and cooperation that is prioritised to refine

and improve, covers: i) coordination in the implementation

of deposit guarantees; ii) the handling of problem banks;

iii) resolution and settlement of failed banks; iv) follow-up

measures for a bank that has had its license revoked; v)

determination of the normal rate when settling guarantee

claims; and vi) coordination for other task implementation,

including confidential information and data. The joint

decree was subsequently translated into implementation

guidelines.

In the middle of 2010, Bank Indonesia, the Ministry

of Finance and the Deposit Insurance Corporation signed

a memorandum of understanding regarding coordination

under a framework of creating and maintaining financial

system stability. The memorandum of understanding was

signed to support policy synchronisation and harmonisation

as well as effective, transparent and accountable

joint actions in the financial sector. Notwithstanding,

implementation coordination and synchronisation

between the three related institutions pays due regard

to the individual tasks and authority of each respective

institution.

The memorandum of understanding between

Bank Indonesia, the Ministry of Finance and the Deposit

Insurance Corporation signed on 30th July 2010 is

part of the financial system safety net, which contains

coordination to prevent and resolve disruptions to financial

system stability. The memorandum of understanding is

important because it coordinates the actions that must

be taken by each respective institution in the event of

a crisis. This is critical because the draft financial system

safety net bill has not yet been adopted by parliament;

thus, the MoU is temporarily functioning as the de facto

guidelines for coordination and cooperation between

the authorities/institutions responsible for implementing

the financial system safety net as well as creating and

77


maintaining financial system stability. In broad terms,

the scope of the memorandum of understanding covers

effective implementation for the following:

financial system stability, which is the task

and responsibility of the Ministry of Finance,

Bank Indonesia and/or the Deposit Insurance

Corporation.

financial system conditions that are suspected by

a respective institution of potentially disrupting

financial system stability.

concerning the measures and actions required in line

with the tasks and responsibilities of each respective

institution.

egislative synchronisation and harmonisation, which is

the task and responsibility of each respective institution,

required to shore up financial system stability.

sector.

Under a f ramework of enhancing pol icy

synchronisation and harmonisation, the exchange of

adequate and accurate data and information through

coordinated collaboration is crucial. Therefore, the

memorandum of understanding also controls the exchange

of data and information required to maintain financial

system stability, including macroeconomic indicators

and micro data from each respective financial sector as

well as other data and information that is required in

the task implementation of each respective institution

pursuant to prevailing regulations. Meanwhile, in relation

to preventing and handling crises in the financial sector,

the memorandum of understanding also stipulates the

obligations of each respective authority/institution when

the final part of the memorandum of understanding deals

with coordinating public communications in relation to

financial system stability. This means that maintaining

public confidence (the general public, investors and

customers) cannot be sidelined when creating and

preserving financial system stability.

Figure 4.1Institutional Cooperative Links

-

--

Ministry ofFinance

Deposit Insurance

BI

FINANCIAL SYSTEM STABILITY

The existing joint decrees and memorandum of

understanding are expected to refine cooperation and

coordination between the three authorities/institutions

in the financial sector, both technically as well as high-

level. Looking at the upcoming challenges faced, closer

cooperation is required in line with increasingly complex

financial sector development and tighter integration

between the domestic financial market and the financial

markets of other countries.

4.3. CRISIS MANAGEMENT PROTOCOL (CMP)

A crisis management protocol is necessary as a strong

legal foundation to boost efforts to build a solid financial

system that is prepared to face a crisis. Bank Indonesia

formulated such a crisis management protocol in 2008,

is a framework and mechanism to prevent and resolve a

safety net. These guidelines were contained within the

draft financial system safety net bill and memorandum

of understanding regarding coordination to create

and maintain financial system stability agreed by Bank

78


Indonesia, the Ministry of Finance and the Deposit

Insurance Corporation. With the draft bill still pending,

the memorandum of understanding has become the

guidelines and critical legal foundation because it sets forth

inter-authority/institution coordination in the financial

to synergise perceptions and understanding in the face

of a crisis. The availability of a clear protocol leads to

effective coordination between authorities/institutions and

expeditious crisis resolution is made possible.

In general, the scope of crisis management protocol

incorporates: i) banking sector crisis prevention; ii) crisis

resolution; and iii) coordinated monitoring and crisis

and parameters that are sources of vulnerability in the

financial sector, which can signal a forthcoming crisis in

the banking sector. Sources of vulnerability can stem

from four triggers: financial institution disruptions; market

disruptions; financial infrastructure disruptions; and

large-scale operational disruptions, for instance natural

disasters or political upheaval. Shocks that befall a financial

institution and the market are quantitative because the

indicator can be measured. Conversely, shocks to financial

infrastructure and operational disruptions are qualitative.

The decision-making process during a crisis is based on

analyses of the combination of shocks that occur. Internally,

Bank Indonesia has a legal framework, however, a sound

legal framework is required as a foundation to legitimise

the decision-making process between institutions in the

implementation of their respective policy response.

Observing developments that have taken place in the

financial sector, in particular experience gleaned from the

A number of amendments were made including the

use of macroprudential indicators as early warning

indicators. The global crisis taught us that the indicators

used for macroprudential policy are important analysis

tools, especially in terms of avoiding systemic impact

in the financial system. Additionally, macroprudential

indicators can also be used to limit the rapidity and depth

of downswings during recession conditions. Currently,

the use of macroprudential indicators in a number of

emerging countries, including Indonesia, remains in

the developmental phase. Looking ahead, updating

crisis detection indicators will continue in line with the

development of diverse parameters that can trigger a

financial crisis.

79


Box 4.1 Financial Inclusion

INTRODUCTION

the past two decades has, in fact, left large swathes

of the general public behind who do not even have

access to the most basic financial services. Based on a

World Bank publication from 2008, less than half of the

citizens in many developing countries own an account

at a financial institution. In the majority of African

nations this figure is less than one-fifth of households.

Notwithstanding, access to financial services is a critical

aspect of alleviating poverty.

PROBLEM FOCUS

The issues that limit public access to financial

services can be divided into two broad categories,

namely supply-side and demand-side. From the supply

side, geographical conditions, the design and pattern of

service as well as the information gap are the primary

factors to low public access to financial institutions.

Conversely, on the demand side, relatively poor

education and knowledge of financial aspects as well

as limited bank engagement with its customers, which

is generally controlled by strict legal requirements

(like business licenses, certified collateral, etc.) leave

the general public in the dark regarding financial

services. Circumstances are compounded further by

self-exclusion or reluctance to seek financial services

because it is believed that conventional bank lending

rates are usury.

ACCESS TO WHAT?

Citing a 1995 World Bank report, at least

four types of financial services are required by the

community in order to have a more prosperous life,

namely deposits, credit services, a payment system

and pension funds. These four aspects are basic

requirements for all human beings.

Although various models of informal microfinance

and spontaneous institutions exist to serve society,

principally in developing countries, as an alternative

financial institution informal microfinance can only

fulfil a small portion of the public’s needs. In this

context, close cooperation between formal financial

institutions, like banks, and microfinance institutions

is key to realising inclusive financial institutions for all

strata of society.

NATIONAL FINANCIAL INCLUSION

Broadening public access to financial institutions

is a complex issue that requires cross-sectoral

coordination involving banking authorities, non-bank

financial services and other departments involved with

“Financial inclusion will link the previously excluded group with the formal economy and they

will eventually contribute to country’s economic growth” and it “...cannot stop at just opening a

saving account and obtaining micro-credit.” President RI Susilo Bambang Yudhoyono

80


poverty alleviation and education, thus, comprehensive

policy is required in the form of a national strategy

for Indonesia. In this context, five pillars of financial

inclusion have been formulated at follows (Figure

4.1.1):

broaden public knowledge of financial products

and services.

boost public capacity from previously unfeasible

to feasible in terms of obtaining financial

services.

financial inclusion program is not separate

from government and Bank Indonesia policy

support.

Figure Box 4.1.1Five Pillar of Financial Inclusion

outreach of financial institutions through agent

banking, phone banking and mobile banking,

etc.

By broadening public access to financial

services, previously unbanked strata of society will

be able to enjoy the benefits of services like savings.

With this pattern of saving in the community,

financial institutions will become more accustomed

to their customers’ needs and, hence, open further

opportunities for financing to prospective customers.

In addition, simple access to payment system services

will boost the efficacy of economic transactions, even

to citizens in remote areas. Furthermore, buying and

selling becomes more efficient and the community

Deposit CreditPaymentSystem Insurance

Access forPoor Productive Community

to Bank and Non Bank Financial Institutions

Financial Education

Financial Eligibiity

Supporting Policy/regulation

Intermediation Facilities

Distribution

- Agent Banking

- Phone Banking

Pillars

- Mobile Banking

*) For example: commercial paper, mutual funds, etc

Blue bullet point : Program already implemented by BI

Yellow bullet point : Program to be implemented by BI

Red bulletpoint

: Program relevant to institutions/institutesoutside of Bank Indonesia

No bulletpoint

: Irrelevant pillar to activity

Note :

Infrastructure

Other financialservice to small and medium businesses*)

81


can benefit from technological advances like cellular

telephones to purchase raw materials from farmers in

remote regions, for example. Farmers will no longer

be forced to sell their produce at low prices as the

result of traders only brining limited cash; they will be

able to use e-money. This is an example of how these

kinds of ventures can stimulate economic activity and

raise living standards. Likewise, insurance services. The

availability of micro-insurance will help members of

the community in the event of a problem that can be

resolved with insurance. Holistically, this is expected

to ameliorate public welfare through participation in

sustainable economic activities.

82


Box 4.2 Green Banking

GREEN BANKING POLICY

Rising temperatures on earth as a result of

industrial activity that releases CO2 gas into the

atmosphere has impacted the issues of climate change

and global warming. Greater public awareness,

including entrepreneurs and the banking industry,

as managers of development financing, is a positive

response to the changes afoot. The fallout from

climate change can significantly impact environmental

conditions and can spread unnoticed to industrial and

economic activity.

The banking industry, in its role to support

national development, embraces a strategic function

because economic growth and the application of

sustainable development principles12 relate to the

allocation of credit. In their position as fund providers

to finance projects, banks are often faced with a

number of policy constraints linked to social issues

and the environment. As providers of funds, banks

also possess a great opportunity to foster compliance

to project management principles in accordance with

social norms and the environment. Bank Indonesia,

in order to maintain financial system stability, drives

banking system development based on Indonesian

Banking Architecture (IBA). In IBA, a national banking

system that is sound, robust and efficient is supported

by six pillars; the second being ‘an effective regulatory

system’ and the fourth ‘a robust banking industry’,

which are particularly relevant to green banking.

and management is also connected to maintaining

financial system integrity. Financial integrity is one

part of the financial trinity (financial stability, financial

integrity and financial inclusion), which covers the

scope of bank efforts to avoid money laundering and

financing terrorism. As an industry without any direct

impact on the environment, avoiding involvement

in environmental pollution and destruction is what

the banking sector can achieve to maintain financial

integrity. In addition, allocation of bank credit to

environmental projects like recycling, hydroelectric

power, reforestation and natural conservation needs to

be increased. This further supports financial inclusion.

Therefore, banks need to support environmental

protection and management as one principle (planet)

of sustainable development.

A number of foreign banks that operate in

Indonesia as well as domestic banks have already

introduced green banking on a voluntary basis through

13 Foreign

banks tend to follow these principles based on the

requirements of their head office in order to meet green

banking standards in their home country. Despite a

significant differences between the green banking

standards practiced at foreign banks compared to

12 Sustainable development is development that respects the interests of the economic,

evaluating and managing social and environmental risk in financing. This standard,

not extend financing to debtors that do not or can not comply with social and

revised in 2006.

83


national banks, which remain defensive (only reacting in

the event of an environmental problem).14 The largest

contribution that can be offered by banks in terms of

environmental protection and management is through

financing practices. The agricultural sector, mining,

manufacturing industry, utilities, and construction are

all economic sectors that have or potentially have an

impact on the environment. Environmental impacts

stemming from businesses financed by banks can

intensify credit risk and tarnish the reputation of the

banks in question. Therefore, active participation is

required from the banks to restrict the environmental

impact of its financing, particularly in relation to bank

risk management.

Bank Indonesia encourages banks to adhere

to environmental aspects through Bank Indonesia

Circular No. 212/9/UKU, dated 25th March 1989

regarding investment credit and capital investment

at commercial banks where banks are obliged to

assess prospective debtors with reference to efforts

to maintain the environment. The new green banking

policy was designed to fit the latest requirements

pursuant to new laws, namely Act No. 32, 2009,

as well as to ensure banks actively contribute to

sustainable development. In its preparation, Bank

Indonesia collaborated with the Ministry of the

Environment to ensure that all infrastructure required

to uphold supporting regulations for green banking can

be applied. Such cooperation is basically the renewal

of existing coordination through a memorandum of

understanding between the Minister of the Environment

and Governor of Bank Indonesia regarding Expanding

the role of the Banking Sector to support Environmental

Management (No. B-07/MENLH/09/2004; No. 6/66/

Regulations on green banking guarantee a level

playing field in the allocation of credit by banks. The

standards set in green banking will later guarantee

that a business failing to meet the environmental

requirements will not receive any support from the

national banking industry across the board prior to

risk mitigation of potential environmental impacts.

In this context, the determination of potential

environmental impacts refers to the requirements

and methods stipulated in the regulations pertaining

to environmental protection and management, the

expertise for which is found at the Ministry of the

Environment.

One principle that will be incorporated into

green banking is bank efforts to manage risk that

could emerge indirectly from credit allocation to

businesses with a potential impact on the environment.

This will be seen as a cost that should be passed on

to the debtor. For the debtor, this cost is an effort

to internaliseenvironmental aspects in economic

development. Given the demands on banks to assess

potential environmental risks from the businesses they

finance, boosting bank capacity and offering assistance

from environmental consultants is necessary in the

implementation of green banking. Efforts to enhance

bank capacity as well as preparing a directory of

environmental consultants is part of the work program

set forth in the memorandum of understanding

between KLH and Bank Indonesia. Holistically, the

scope of the work program covers aspects of legal

instruments, the provision of information, education

and socialisation activities, as well as joint research. 14 The implementation of green banking is phased according to the actions taken by

the bank in question: i) defensive banking; ii) preventative banking; iii) offensive banking; and iv) sustainable banking (Bouma et al 2001).

84


REFERENCES

Bank Indonesia, Indonesian Banking Architecture,

2004

Bouma, JJ., Marcel Jeucken and Leon Klinkers

(2001),“Sustainable Banking - The Greening of

industrybenchmark for determining, assessing

project financing”, July, http://www.equator-

principles.com

85

Chapter 5. Financial System Stability Challenges and Prospects

Chapter 5Financial System StabilityChallenges and Prospects

86



87


Chapter 5 Financial System Stability Challenges and

Prospects

Potential vulnerabilities on the global financial market continue to overshadow

the economic recovery in advanced countries, while the ability of emerging

market countries to overcome the adverse effects of foreign capital inflows,

coupled with internal challenges like mounting inflationary pressures, will

threaten financial system stability looking ahead. Notwithstanding, economic

predictions for Indonesia remain sufficiently optimistic, accompanied by

growing investor confidence buoyed by the stable exchange rate that will

drive the banking sector, stock market and SUN market to perform better.

Against this propitious backdrop, the outlook for financial system stability in

semester-I 2011 remains positive.

5.1. FUTURE EXTERNAL AND INTERNAL

ECONOMIC CONDITIONS

Maintained macroeconomic and financial system

stability throughout 2010 has given medium-long-

term economic growth in Indonesia the opportunity to

accelerate further. Economic expansion in 2011 is expected

in the range of 6.0%-6.5%, underpinned by fundamental

factors that will continue to improve and an increasingly

conducive macroeconomic environment (Table 5.1).

From the demand side, investment activity, which

is expected to grow by 10%, will drive economic growth

in 2011 buttressed by 4.4% - 4.9% growth in private

consumption. The sustained torrent of foreign direct

capital flows during 2010 helped garner such optimism

in future investment growth. In 2010, foreign capital

inflows in the form of foreign direct investment (FDI)

reached US$ 9,836 million, far in excess of that received

in 2009 amounting to just US$ 2,628 million. This trend is

expected to persist in the subsequent year, particularly on

the back of potential foreign capital flows to emerging and

developing countries as an impact of the crisis affecting

Europe and the languid US recovery. Domestic economic

growth will be more sustainable if based on investment

activity as the motor.

2011*2010

GDP (% yoy) 6,1 6,0 - 6,5

Inflation (%, end of period) 6,96 5 ± 1

Table 5.1Projections of GDP and Inflation

*) Bank Indonesia projections

88


have the potential to affect demand for production goods

in Indonesia. Consequently, export growth in 2011 is

predicted to be lower than that in 2010.

Internally, inflationary pressures will continue to

blight the economic landscape of Indonesia in the year

ahead. Inflationary pressures will primarily originate

from volatile foods due to limited supply as a result of

weather anomalies; the plan to set a minimum rice price;

planned price hikes for the most expensive fertilisers; the

soaring trend of global food prices due to strong demand

stemming from the global economic recovery; as well as

limited food exports from key exporters like Thailand.

Additionally, potential inflationary pressures from rising

non-subsidised fuel prices and restrictions on their use

require continuous monitoring.

The uptrend in commodity prices will require

additional attention in order to maintain financial system

stability. Currently, the effects of soaring food prices are

the focus of central banks around the world in relation

to controlling inflation. Monetary authorities worldwide

are predicted to introduce a number of follow-up policy

measures that impact the domestic financial market and

potentially affect the balance of global financial markets.

In addition, a concentration of funds is expected on

commodity markets as long as expectations persist of

soaring commodity prices. Furthermore, financial market

conditions in established countries will continue to be

marred by uncertainty surrounding crisis resolution in

Europe, which is predicted to intensify price volatility on

global financial markets and be exacerbated by the global

flow of funds that always seeks safe investments with the

highest returns.

5.2. FINANCIAL SYSTEM STABILITY CHALLENGES

Looking ahead, potential vulnerabilities on global

financial markets will continue to face uncertainty

regarding the economic recovery in advanced countries,

2011*2010

Private consumption 4,6 4,4 - 4,9

Government consumption 0,3 9,7 - 10,2

Gross fixed capital formation 8,5 9,6 - 10,1

Exports of goods and service 14,9 8,5 - 9,0

Imports of goods and services 17,3 9,8 - 10,3

Table 5.2Economic Growth in Indonesia according to Type

*) Bank Indonesia projections

Source: World Economic Outlook, IMF, January 2011

Figure 5.1.Flow of Direct Investment

Million USD

Direct Investment Portfolio Investment Financial Transaction

8.000

6.000

2004

Q1

2005

Q1 Q3

2006

Q1 Q3

2007

Q1 Q3

2008

Q1 Q3

2009

Q1 Q3

2010

Q1 Q3

4.000

2.000

-2.000

-4.000

-6.000

-8.000

-

Figure 5.2Growth Projections for several Countries

0

2

4

6

8

GlobalGrowth

AdvancedEconomies

Emerging and DevelopingEconomies

ASEAN+5

2010 2011 2012

Looking ahead, however, the Indonesian economy

will be beset by a number of risks. Externally, risks will stem

from the lacklustre economic recovery process with fears of

a further slowdown. Global economic growth is projected

at around 4.5% in 2011, which is down compared to

that posted in 2010 at 5.0% (Figure 5.2). Such conditions

89


as well as the ability of emerging market countries to

mitigate the adverse effects of foreign capital inflows. A

number of austerity programs in countries affected by fiscal

difficulties, particularly advanced countries, will encourage

weak global demand. Ultimately, this will not only affect

export-oriented economies but all economies across the

globe. Improvements rely heavily upon government

strategy to drive domestic demand as well as steer foreign

trade towards countries that have already recovered. In this

context conditions in ASEAN+3 are sufficiently conducive,

with interregional trade between member countries

performing well.

Improved economic conditions in a number of

advanced countries will slightly ease the influx of foreign

capital flows to emerging market countries. This will

intensify asset price volatility on global financial markets

and precipitate a new equilibrium. However, the interest

rate differential between established countries and

emerging market countries will remain the principal reason

why global investors favour emerging markets. Therefore,

global investors are expected to become more cautious and

selective and choose countries with solid fundamentals,

particularly when there is the threat of risk from an asset

price bubble. Clearly, this represents an opportunity for

emerging market countries to enhance their economic

fundamentals. Eventually, investors will prefer longer-term

investments, hence, reducing the risk of sudden reversal.

Domestic economic fundamentals are stable and

growing, while the economies of advance countries are

tending to slow due to the pass-through effect of the

sovereign debt crisis in Europe as well as Quantitative

Easing II introduced by the Federal Reserve and the fiscal

stimuli in Japan, therefore, foreign capital inflows are

expected to persist in 2011. The continuation of foreign

capital inflows to Indonesia will spur further rupiah

appreciation in 2011. Rupiah appreciation reduces the

cost of imported goods and services, which will ease

inflationary pressures on the import side. This will also

reduce the costs of production for companies that utilise

imported raw materials and increase the opportunity for

such firms to boost production, consequently, improving

their financial performance and debt repayment ability,

which lowers corporate non-performing loans. In addition,

the persistence of foreign capital inflows will contribute

to increased liquidity in the economy and larger foreign

exchange reserves in 2011. Taking the factors mentioned

into consideration, coupled with fiscal sustainability and

a relatively stable political situation, sovereign risk in

Indonesia is expected to ease, thus, achieving investment

grade in 2011.

However, a number of issues require close monitoring.

The prevalence of short-term foreign capital inflows could

raise the JSX composite and other financial assets like

government bonds. Rapid capital market growth as a

result of the inundation of short-term foreign capital flows

could distort fundamental prices and real prices, thereby

creating a stock market bubble, which would increase

potential pressures on inflation from the perspective of

asset prices. Moreover, short-term foreign capital inflows

are vulnerable to a sudden reversal, which has the potential

to compound liquidity risk and undermine the exchange

rate, in turn increasing potential sources of financial system

instability.

Inflationary pressures stemming from future non-

subsidised fuel price hikes and restrictions on their use

could potentially raise bank lending rates, which will

ultimately amplify bank credit risk through an increase in

non-performing loans.

Furthermore, higher lending rates will stifle credit

growth and encourage bank disintermediation. A

subsequent potential decline in credit allocation,

particularly to the agricultural and plantation sectors due

to unpredictable climate conditions, would also heighten

credit risk and, eventually, banks would tend to avoid these

90


sectors. However, government policy as stipulated in Act

No. 41/2001 regarding farmland protection represents an

opportunity for banks to finance the agricultural sector.

This law ensures synergy between the central and local

governments when implementing regulations linked

to farmland protection, for which the government has

allocated Rp3.4 trillion.

5.3 FINANCIAL SYSTEM STABILITY OUTLOOK

Considering the factors elaborated upon in this

chapter, the outlook for the financial system in 2011 is

expected to remain positive. Impressive bank performance

during 2010 in terms of resilience and financing will be

bolstered in 2011 by increasingly conducive macroeconomic

prospects, which will expand the intermediation function

in the following year. Improved macro conditions and

better risk management will raise asset quality, particularly

credit, as indicated by a downward NPL trend. The positive

economic outlook is also expected to contribute to less

stock market volatility and a lower bond yield, thereby

underpinning financial system stability with a financial

stability index projected in the range of 1.36-1.83 at the

end of semester-I 2011 and a baseline of 1.60 (Figure

5.2).

91

Article 1. Risk Behaviour in the Monetary Policy TransmissionMechanism in Indonesia

Article

92



93


Risk Behaviour in the Monetary Policy TransmissionMechanism in Indonesia

Article 1

Doni Satria*, Solikin M. Juhro**

This study explores interconnections between risk behaviour in the financial sector, particularly the banking

sector, with monetary policy stance. Referring to the modified model developed by Bernanke and Blinder (1988)

for analysing bank lending behaviour, we develop an empirical model to test the role of risk behaviour in the

monetary policy transmission mechanism. Vector Error Correction Model is applied to test the significance of

interaction between risk variables and monetary policy stance in the short-run dynamics of credit behaviour

around its long-run cointegration with real GDP. Some empirical results emerge from this preliminary study.

First, there are early indications that the risk-taking channel in the monetary policy transmission mechanism

exists in Indonesia during the analysis period. Second, risk variables and credit tend to move procyclically

while monetary policy stance is more a-cyclical. Third, the procyclical behaviour of credit and risk variables

reverses the effect of loose monetary policy stance, and there is an indication of asymmetric effect between

tight monetary policy and loose monetary policy in the Indonesian economy. These empirical findings bring

about policy recommendations for better understanding of risk behaviour in the banking sector, as well as

integration between monetary and financial sector policies.

JEL Code : E52, E58, Key word: Monetary Policy Transmission Mechanism, Monetary Policy Stance, Banking

Risk Behaviour, Risk Perception.

I. BACKGROUND

The effect of risk behaviour on the dynamics of the

financial sector is a common research topic raised these

days, particularly that relating to the efficacy of policy

response taken in the face of the global financial crisis

which struck in the middle of 2007. Several arguments

have emerged to observe basic contributing factors

behind the financial crisis, which became known as an

unprecedented crisis in terms of its depth and duration.

Taylor (2009) found that the crisis was caused by central

bank policy that tended to maintain excessively low interest

rates in response to prolonged low inflation prior to the

crisis. Taylor postulated that central banks in advanced

countries failed to calculate risk in the banking sector

as a reaction function to monetary policy, thus resulting

in an inappropriate nominal interest rate (too low). The

implications of this analysis indicate interaction between

monetary policy stance taken by the central bank and risk

in the financial sector, particularly the banking sector.

Meanwhile, Mishkin (2009) found that monetary policy

is potentially more effective during a crisis compared

* Teaching staff at the Faculty of Economics, Padang University.** Economic researcher at Bank Indonesia and postgraduate teaching staff at the Faculty of

Economics, University of Indonesia. The opinions expressed in this paper are those of the authors and do not represent the views of the institutions where they work

94


to normal conditions, hence providing a foundation for

macroeconomic risk management to confront the problem

of economic contraction during the crisis period.

These facts evidence a correlation between monetary

stability and financial sector stability. How monetary

authorities should respond and act in their monetary policy

can generally be understood, however, there is relatively

little debate among economists. Notwithstanding, how

monetary authorities should respond to problems that

emerge in the financial sector remains a hot topic of debate

among economists (Goodhart and Tsomocos, 2007). The

target of monetary policy for the monetary authority is

easy to accomplish if financial sector stability is maintained.

Conversely unstable macroeconomic fundamentals will

trigger shocks in the financial sector. The relationship

between monetary stability and financial sector stability

is ultimately a central issue in terms of observing

correlation between the policy taken, risk behaviour and

the duration of a financial crisis. Nier and Zicchino (2008)

found that bank credit supply is influenced by monetary

policy stance, which interacts with balance sheet stress,

transmitted through bank losses. The research concluded

that interaction between monetary policy stance and bank

losses is stronger during a crisis, with the assumption that

the magnitude of risk increases during an economic crisis.

This implies that risk in the financial sector interacts with

monetary policy stance. In the case of Canada, Li ad St-

Amant (2010) found that tight monetary policy (contractive)

has a stronger effect on output compared to expansive

monetary policy. Conversely, expansive monetary policy

has a stronger effect than contractive when escalating

financial pressures (risk) beset an economy.

Borio (2008) discovered the importance of analysing

the risk-taking channel in the monetary policy transmission

mechanism. This differs from the bank-lending channel

revealed by Bernanke and Blinder (1998) as well as

Bernanke and Gertler (1995) who found that monetary

policy works through bank reserves and, subsequently,

influences the supply of credit in an economy. The risk-

taking channel affects bank credit supply through the

banks’ decision to extend credit based on changes in bank

behaviour to confront credit risk. Adrian and Shin (2009)

found that the risk-taking channel also differs from the

financial accelerator concept posited by Bernanke and

Gertler (1999). In this context, the results of empirical

research provide evidence for the existence of the risk-

taking channel in monetary policy transmission1.

In the context of the Indonesian economy, in-depth

observations of the role played by risk factors in the

financial sector on the transmission mechanism have not

been undertaken. Goeltom et al. (2009) concluded that

based on empirical analysis, risk perception plays a role in

transmitting monetary policy in Indonesia. Based on the

conditions and complexities faced by Bank Indonesia in

terms of instituting monetary policy, this research identifies

the asymmetric effects of monetary policy. Asymmetry is

affected by financial sector behaviour that has a propensity

to be procyclical as well as the presence of the risk-taking

channel as proposed by Borio and Zhu (2008).

The explanation provided in this section does not

directly indicate interaction between monetary policy and

risk in the banking sector, which is transmitted to the real

economy though the supply of bank credit2. Indonesia, as

a country with a financial sector that has not developed as

rapidly as that found in more established countries, does

not have many alternative forms of investment financing

and, therefore, the role of the banking sector remains

dominant in the financial sector. Accordingly, a study

1 Refer to Gambacorta, 2009 and the References contained within.2 Bernanke and Blinder (1988) were the first to develop the credit channel in the monetary

policy transmission mechanism. Analyses of how bank credit supply is influenced by monetary policy have unearthed a variety of channels over the past two decades, which are regularly analysed by economists and represent an active research topic in the field of economics. Channels of monetary policy transmission through bank credit have been identified asthe liquidity channel (Diamond and Rajan, 2006), Bank Capital Channel (Van der Hauvel, 2007) and the Risk-taking channel (Borio, 2008 and Adrian and Shin, 2009). The three channels identified affect the real economy through changes in credit supply from the banking sector, which subsequently influences real expenditure, investment and consumption.

95


in to how bank risk impacts the Indonesian economy is,

therefore, pertinent and timely in the context of preserving

financial sector stability3.

This research strives to observe the relationship

between financial sector risk, particularly banking, and

monetary policy as well as the implications on the monetary

policy transmission mechanism to the real sector. Hitherto,

the majority of analyses on financial sector stability focuses

on identifying factors that determine financial sector risk

and institutional factors that determine risk profile in the

financial sector. Meanwhile, feedback from changes in risk

in the financial sector on the real economy has not been

thoroughly modelled (Tieman and Maechler, 2009). By

understanding the magnitude of influence of risk changes

in the financial sector, especially the banking sector and

the interaction between monetary policy and bank credit

supply, this research will provide an illustration about

the real effect of changes in risk and risk perception in

the banking sector as well as monetary policy (which is a

reflection of risk-taking behaviour by economic players)

on the economy.

An empirical specification model is built in this

research using the modification model developed by

Tieman and Maechler (2009). In general, the empirical

model will test the impact of risk behaviour, reflected by

risk perception (risk aversion) of economic players and the

level of risk in the banking industry, which interacts with

monetary policy stance and bank credit supply. The most

salient conclusions drawn from this research are that risk

perception and the level of risk in the banking sector play

a significant role in transmitting monetary policy through

the credit channel in Indonesia. In this context, as risk

perception and the level of risk in the banking sector

interacts with monetary policy stance they trigger a reversal

in the impact of loose monetary policy. Oppositely, a tight

monetary policy stance taken to contract the economy

through the bank-lending channel becomes less effective

as it interacts with the variables of risk perception and level

of risk in the financial sector.

This paper is divided into five sections. After the

Background, the second section briefly presents the

theory associated with credit market equilibrium and

the role of risk variables as push and pull factors of bank

credit expansion. The third section explains the research

methodology; in particular in terms of developing the

empirical model estimated using the vector error correction

model (ECM). The subsequent section presents the results

of the estimations and analyses on the impact of risk and

monetary policy stance on the dynamics of bank-lending

performance. The final section includes several conclusions

and policy implications.

II. THEORY

Most economists believe that banks or financial

intermediaries play a significant role in the economy

through the transmission of monetary policy. However,

consensus has not been reached on the way in which banks

transmit monetary policy to the real economy. Therefore,

this remains a crucial research topic in the field of monetary

economics. The preliminary approach in explaining the role

of banks in transmitting monetary policy is believed to be

through the money channel or liabilities of the banking

sector on the economy (money view). Subsequently, ideas

developed further that banks influence the economy

through the credit channel (Bernanke and Blinder 1988). It

is speculated that monetary policy can affect the economy,

in terms of the credit channel, through credit supply

from the banking sector or the bank lending channel,

and through the balance sheet where monetary policy

influences the ability of firms to obtain external sources of

financing, otherwise knows as the balance sheet channel

(Bernanke and Gertler 1995).3 Existing literature indicates a tendency to analyse factors that affect bank risk, disregarding

how bank risk influences the real economy in terms of transmission through the supply of bank credit.

96


Based on the preliminary theoretical model developed

by Bernanke and Blinder (BB), a theoretical model can be

developed to input the role of financial sector risk, in

particular the banking sector, to analyse the presence

of a risk channel in the monetary policy transmission

mechanism. Dynamic model development is broadly based

on the BB model, similar to that developed by Escandon

and Diaz-Bautista (2000) and Walsh (1998), which can be

used as a benchmark for the development of the empirical

model used in this research.

In the dynamic version, the commodity demand

curve and credit ‘CC” in the BB model are transformed

to become the long-term and short-term adjustment

process between aggregate demand and supply in the

real sector4 .As prices are assumed constant, short-

term adjustment occurs through the excess demand

mechanism, which returns output to equilibrium. These

conditions can be expressed as follows:

4 Standard IS/LM models in macroeconomic textbooks use the assumption of perfect substitution between bonds and bank lending. The Bernanke and Blinder (BB) model discards that assumption and they form an IS/LM equilibrium model by incorporating the bank credit market in to the model. In the BB model, demand for bank credit is a function of the lending rate, market rate (thus the bond rate) and level of income, hence the model uses the CC curve as a substitute for the IS curve (Refer to Bernanke and Blinder 1988).

As in the BB model, aggregate demand (yd) is

determined by the bank lending rate, market rate and

fiscal policy. Also as is found in the BB model, the market

rate is determined by monetary policy (bank reserves, R)

and money demand, therefore:

(2.1)

(2.2)

(2.3)

Equations 2.4 to 2.6 indicate that equilibrium on

the bank credit market through the bank credit price

adjustment mechanism (lending rate), credit demand

determined by the bank lending rate, market rate for

bonds, level of the real economy and demand-side credit

risk. Furthermore, the bank lending rate, bond market rate

and level of risk when allocating bank credit influence the

demand for bank lending.

In addition to the risk variables, all other variables

included in the analysis model,based on the model

developed by Escandon and Diaz-Bautista (2000), are the

same as the BB model (1988). The analysis conducted

by Escandon and Diaz-Bautista does not explain the

theoretical basis of including demand risk and credit supply

in the model. Freixas and Jorge (2008) and Disyatat (2010)

developed a more in-depth explanation that justifies the

use of risk variables as a determinant of bank credit supply

and which subsequently interacts with monetary policy.

A matrix solution was used for both equations in the

differential equation system when developing a hypothesis

to discover the respective impact of each variable on

the exogenous change in risk in the allocation of bank

credit.

(2.4)

(2.5)

(2.6)

The basic theory in this model can be obtained from

the solution as a tool to form a hypothesis from economic

theory that will be tested using the empirical model. In

the long term, economic variables tend towards a new

equilibrium after a shock to the exogenous variable. In

this model the exogenous variables are fiscal policy (G),

Financial sector dynamics stem from shifts in

bank lending rates ( ) that balance the bank credit

market. Assuming no credit rationing, this variable will

alter excess demand and excess supply on the bank

credit market, thus:

97


monetary policy (P) and risk ( ). The partial impact of risk

can be written as follows:

conclusion drawn by these two models differs from the

preliminary conclusion found by Bernanke and Blinder

(1988). In the BB model, monetary policy determines the

amount of credit extended by banks through a decline

in deposits (and bank reserves) that can be accumulated

by the bank in order to allocate as credit to the business

community. Meanwhile, empirical facts in the economy

demonstrate that banks can use other sources of funds than

deposits (for instance through interbank loans), therefore,

the working mechanism in the two models in terms of

monetary policy to determine bank loans is through changes

in risk faced by the bank in terms of obtaining funds from

the interbank money market. Meanwhile, the inclusion of

deposits in the model developed by Disyatat (2010) was

attributable to inside money. The conclusion from this model

indicates that the role of bank lending in the transmission

of monetary policy is important in the economy despite

the expanding role of the non-bank financial sector as an

alternative source of investment funds.

III. METHODOLOGY

3.1. Real Impact of Bank Risk and Monetary

Policy on the Economy

Theoretical studies demonstrate that monetary policy

has a real impact on the economy through bank lending.

Furthermore, literature regarding the monetary policy

transmission mechanism indicates that the financial sector

affects the economy through the credit channel allocated

to the real sector. The results of theoretical studies evidence

a long-term relationship between bank lending and

the economy, which empirically indicates cointegration

between total real credit extended by the banking sector

and real economic production.

The implications of this theoretical model reveal that

there are short-term dynamics in risk change behaviour in

the supply of bank lending that interacts with monetary

policy. Such conditions determine shifts in bank lending,

Exogenous shocks stemming from changes in risk

from the supply and demand of bank credit are transmitted

through a shift in equilibrium on the bank credit market.

This has an important implication for the economy, namely

that if the risk faced by banks escalates, then supply-side

credit risk also increases, which raises the cost of bank

lending and lowers the level of economic production

(GDP or output) in the long term. Freixas and Jorge (2008)

developed a theoretical model of the monetary policy

transmission mechanism through risk using a partial

equilibrium approach on the interbank money market.

In general, the model explained that the monetary policy

instituted by a central bank determines the availability of

liquidity on the interbank money market, which forces

banks with liquidity shortfalls to ration credit offered

to their customers (credit rationing), thereby spurring

a production increase or decrease in the real sector.

Imperfect information on the interbank money market is a

source of risk and causes monetary policy to have a larger

impact compared to when perfect information is available.

This theoretical model provides justification for monetary

transmission through the bank-lending channel without

having to assume an absence of credit rationing on the

bank credit market. Therefore, the hypothesis obtained

based on the solution in the previous dynamic version of

the BB model is still valid in this research.

The risk model for the credit channel in the monetary

policy mechanism developed by Disyatat (2010) also drew

a relatively similar conclusion to that of Freixas and Jorge

(2008). In this model it was found that the risk mechanism

is a push and pull factor of bank lending expansion. The

(2.8)

98


thus, risk change behaviour on the supply side, at least in

the long term, has an impact on the economy through

changes in real credit allocated by the banking sector.

The empirical analysis conducted in this research uses two

indicators of risk behaviour in the banking sector. The

first provides a measurement for the level of risk aversion

of banking sector players in terms of asset management

with the assumption that they optimally allocate their

assets. The second indicator shows the level of risk in the

banking industry.

Accordingly, an econometric specification model can

be used to specify the error correction model as follows:

with the indicator of monetary policy stance. With the

theoretical assumption that long-run cointegration exists

between credit and GDP then Equation 3.1 is estimated

using VECM, as developed by Johansen5. The advantage

of using VECM is the possibility of observing the error

correction term in short-term dynamics of the two-way

interaction between credit and GDP in one model system.

Therefore, it is possible to reveal which credit variable is

weakly exogenous to GDP. If the estimation results show

that credit is weakly exogenous in the short-term dynamics

of GDP of the VECM mechanism used, then no feedback

is occurring from changes in credit to GDP.

To run estimations using Equation 3.1, variables

of bank risk and monetary policy stance are required. In

this research two risk indicators are used as independent

variables in Equation 3.1. The risk indicators used are bank

risk and risk perception by economic players in the banking

sector. Meanwhile, for the indicator of monetary policy

stance, the difference between the optimal interest rate

(in line with empirical calculations for Indonesia) and the

actual interest rate is used. If monetary policy stance is too

tight, then the actual interest rate will be higher than the

optimal rate and vice versa. If the actual rate is the same as

the optimal rate then monetary policy is neutral. Estimations

for the monetary policy rate utilise Taylor Monetary Policy

Rules, where the model and its variance are both used by

Bank Indonesia to analyse monetary policy in Indonesia.

3.2. Indicators of Risk Behaviour in the Banking

Sector

This research utilises two indicators of risk behaviour

in the banking sector. The first measures the risk aversion of

players in the banking sector in terms of asset management

assuming that their assets are optimally allocated. The

second indicator shows the level of risk in the banking

industry as a whole.

5 Refer to Enders, 2004, pages 362-366

i = 1, 2, 3 (investment credit, working capital

credit and consumption credit)

ii = 1, 2 (tight or loose monetary policy stance)

where:

Cred = real credit extended by banks at a rate equal

to the market rate (investment credit,working

capital credit and consumption credit)

GDP = real GDP

RisktA = Risk perception index of players in

thebanking sector

RisktDD = Level of risk in the banking sector

(distance to default)

StanceK = Monetary policy stance (tight or loose)

Equation 3.1 indicates that changes in bank lending

are determined in the long run by two stationer variables in

first difference, l(1), real bank credit with the real economy,

where the long-term speed (coefficient) of adjustment is

. In the short run, changes in credit are determined by

economic growth in the previous year, risk indicators in the

banking sector, and risk perception in the banking sector,

as well as interaction between bank risk and risk perception

(3.1)

99


3.2.1. Risk Perception in the Banking Sector

The indicator of risk perception in the banking

sector explains bank behaviour in terms of evaluating

risk based on asset allocation theory to minimise risk

with the assumption that banks are risk averse. With the

assumption that banks allocate portfolio in the form of

risk-free assets amounting to 1-y, then the return on total

portfolio is as follows (Bodie et al):

DD shows the amount of standard deviation from the

average value required by the ratio of asset market value

to liabilities for a firm to experience default (Vassalou and

Xing, 2004). Banks are a type of firm that adhere to strict

regulations and have an assets and liabilities structure

that differs from regular firms. A bank will generally have

much larger liabilities than any other firm because they

manage funds from the general public. Consequently,

the calculations used to find DDof a bank also have to be

adjusted. The method developed by Vassalou and Xing

(2004) was used for firms in general and not for banks

in particular, therefore, the formula utilised to calculate

DD was modified in the same way as that developed by

(Chan-Lau and Sy, 2007) as follows:

Therefore, the coefficient of bank behaviour in

determining risk aversion is as follows:

(3.2)

Where, A = coefficient of bank risk aversion, E(rp)-rf

= risk premium (difference between expected return on

risk portfolio and return on risk-free assets), y* = total

bank assets exposed to risk (excluding SBI and SUN), 2p

= variance of return on assets.

3.2.2. Risk in the Banking Sector

In addition to using risk perception indicators, other

risk indicators are also incorporated in this research, namely

an indicator of risk in the banking sector. If the market

value of a company is lower than the value of its liabilities

then it is declared bankrupt/default. By using this concept

the risk of a firm (including banks) can be known by

measuring the difference between the ratio of asset market

value to liabilities compared to default conditions.

(3.3)

(3.4)

PCAR = Minimum CAR (capital adequacy ratio)

pursuant to banking regulations

(3.5)

Where:

DDt = distance to capital

(3.6)

Where, rc = return on total portfolio, rp = return on

risk portfolio, rf = return on risk-free portfolio, thus the

solution to optimal asset allocation for the bank is:

Figure 3.1Indicators of Banking Sector Behaviour

(percentage, normalised)

100


Figure 3.1 shows the calculation results of risk

behaviour. It can be seen that the risk behaviour of

economic players (indicator of risk aversion) and level of

risk in the banking sector (distance to default – inverse)

tends to be low when the risk premium, estimated using

the difference between the lending rate and policy rate

(1-month SBI), is high and vice verse. This indicates that

economic players in the banking sector will respond to

monetary policy tightening by allocating funds to relatively

low-risk portfolio. Meanwhile, when the monetary policy

rate is low, a high risk premium can alleviate the effects

of their high risk perception on their fund allocation

behaviour.

3.3. Monetary Policy Stance

An indicator of monetary policy stance is required in

order to solve equation 3.1. Monetary policy stance, used

as an explanatory variable for the dynamics of short-run

bank lending in this research, is the difference between the

actual policy rate (SBI rate) and estimations using monetary

policy rules. Following Juhro (2009), this research uses data

for monetary policy stance obtained based on estimation

results against empirical equations (Taylor Rules). The

results are modified using classic Taylor Rules, otherwise

known as interest smoothing rules as follows (Clarida,

Galli, Gertler (1997) in Juhro (2009):

This research uses a dummy variable as a reflection of tight

or loose monetary policy stance. In this context, a +/-25

bps difference in the actual rate indicates normal monetary

policy stance, anything outside of this range will indicate

a tight/loose stance.

IV.ANALYSIS

Prior to estimating the empirical model and testing

the hypothesis, data feasibility is tested in the specified

model in order to test the presence and impact of risk

in the monetary policy transmission mechanism through

the bank-lending channel in Indonesia. The tests used

include unit root tests and co-integration tests to discover

long-run equilibrium/correlation between credit and GDP.

Data stationary and co-integration tests found that credit

and GDP are co-integrated, hence analysis using the error

correction model (ECM) is used. The co-integration tests

showed that all three types of credit (consumption, working

capital and investment) have a long-run correlation with

the economy. Therefore, there is a long-term relationship

between bank lending and economic performance.

Analysis results also justify the impact of bank lending on

the economy.

The next phase included an analysis of the relationship

between risk and the short-term dynamics of bank lending.

Then, the presence of a risk channel in the monetary

policy transmission mechanism in Indonesia is analysed

based on the interaction between monetary policy stance

and risk. Results of the Vector Error Correction model

(VECM) indicated that credit is not a weakly exogenous

variable to GDP, thus producing feedback from changes

in credit on GDP dynamics. Congruent with the focus of

this research, empirical findings of endogeneity were only

found on credit, which is examined in greater depth and

presented in Table 4.1.

(3.7)

Where, it is the optimal monetary policy rate, t is

actual inflation, t* is the inflation target, yt is actual GDP

and yt* is potential GDP calculated using the Hodrick-

Prescot Filter.

The recommended interest rate by the Taylor rule

can subsequently be calculated based on the estimation

results. Several economists use a different form as a

measure of policy stance also based on the Taylor rules.

101


IndependentVariable

Investment Credit[Dlog(CR_INV)]

Working Capital Credit[Dlog(CR_MK)]

Consumption Credit[Dlog(CR_KON)]

ECT ( ) -0.082055 -0.293294 -0.091933 [-3.91674]*** [-5.08903]*** [-2.94792]***

Stance+ -0.002575 -0.013970 0.012844 [-0.21983] [-1.20977] [ 1.02093]

Stance- 0.028623 0.030163 0.023414 [ 1.99965]** [ 2.43362]*** [ 1.75151]*

DD -0.001531 -0.002443 -0.000502 [-1.16145] [-1.96550]* [-0.38484]

A 0.002996 0.024078 0.025623 [ 0.24890] [ 1.89834]* [ 2.08346]**

Stance+ *A -0.006795 -0.023959 -0.030875 [-0.52006] [-1.76619]* [-2.39797]***

Stance- *A -0.032355 -0.043594 -0.050885 [-1.91202]* [-2.45359] *** [-3.18399]***

Stance+ *DD 0.001991 0.004052 0.000966 [ 1.25322] [ 2.57037]*** [ 0.58502]

Stance- *DD -0.003427 -0.003327 -0.001984 [-1.86661]* [-2.03648]** [-1.09173]

R2 0.406906 0.523314 0.339990F-test 3.185347*** 5.097017*** 2.391671***

Table 4.1Estimation Results for the Three Types of Credit Extended by Banks

Source: results of processed data

Note: Values in parenthesis are t-value, *** significant at 1%, ** significant at 5%, *significant at 10%, Stance+=Tight monetary policy, Stance-=Loose

monetary policy

From Table 4.1 it can be observed that all short-term

adjustment coefficients are negative and head towards

long-term equilibrium (ECT/Error Correction Term) in

the three credit models, which are significant at a 99%

confidence level. These results show that the model

used was adequately stable and in accordance with the

theoretical foundations. Furthermore, the ECT coefficient

for short-term log GDP was positive and significant.

Meanwhile, in terms of goodness of fit of the models,

the determination coefficients returned values of between

0.33 and 0.52, which is sufficient for a model using first

difference data. The results of testing the F-statistics also

demonstrated that all equations were significant at the

99% confidence level.

4.1. Impact of Risk Behaviour on the Short-Run

Dynamics of Credit

Results of the partial impact analysis of risk behaviour

on the short-term dynamics of credit are presented in Table

4.2. The results indicate that loose monetary policy leads

to significant impacts from risk perception in the banking

sector (A) and risk in the banking sector (DD) on short-

term the dynamics of banking loans (excluding variable

A on working capital credit). Tight monetary policy only

significantly determines the variable of risk in the banking

sector on working capital credit. These results confirm that

in the short term the variables of risk perception and risk

in the banking sector significantly determine the dynamics

of bank lending in Indonesia.

102


The level of risk in the banking sector (which

interacts with loose monetary policy stance) is negative

and significant, denoting that expansive monetary policy

coupled with less risk in the banking sector (DD increases)

will reduce the percentage of bank credit growth for

all three types of loan, ceteris paribus. The implication

of this empirical findings conflicts with the theoretical

analysis conducted in this research. This phenomenon

requires additional testing for a satisfactory explanation.

A temporary explanation is that the interaction of risk in

the banking sector, which is procyclical (+), with monetary

policy that is countercyclical (-) reverses the positive impact

of lower risk in the banking sector on credit growth.

The preliminary argument for such conditions is as

follows. First is an anomaly in the Indonesian banking

industry where although national banks tend to be

inefficient and suffer high risk they continue to enjoy high

margin. Second is the issue of persistent excess liquidity

and the procyclical nature of credit allocated by the

banking sector (Bank Indonesia, 2010). A loose monetary

policy stance (forward-looking) is a signal for banks of a

sluggish economy. Therefore, economic players in the

banking sector tend away from expansion loans but retain

liquidity in the form of risk-free portfolio. Third are the

empirical findings of competitive behaviour in the banking

industry in Indonesia (Ariefianto, 2010). This research

found that banks with a high non-performing loans (NPL)

ratio tend to expand their credit when DD is low (risk is

high) in order to bring down their NPL ratio.

With the exception of working capital credit, the

level of risk in the banking sector was not significant

on the dynamics of short-term bank lending when

monetary policy is tight. The explanation for this is that

countercyclical monetary policy indicates a boom economy

when policy stance is tight, while risk perception and the

level of risk in the banking sector tend to be low when

an economy is booming. Consequently, a higher risk

Tight MonetaryPolicy Stance

Errorstandard

t-count

Tight MonetaryPolicy Stance

Errorstandard

t-count

The coefficient of risk perception in the banking

sector is negative and significant for two types of

credit (investment and consumption). Economically,

when interacting with loose monetary policy stance, an

increasing perception of risk in the banking sector reduces

the percentage of credit allocated ceteris paribus. This

implies that it is necessary for policymakers to understand

the direction of risk perception in the banking sector

when implementing expansive monetary policy because

an increase in risk perception will negate or reverse the

effect of the policy taken through a decline in credit

expansion.

Table 4.2Impact of Risk Behaviour on the Short-Term

Dynamics of Credit

Source: results of processed data

Note: *** significant at 1%, ** significant at 5%, *significant at 10%

-0.003799 0.00526 -0.72224

0.000119 0.00568 0.0205951

-0.005252 0.00504 -1.04026

0.00046 0.00098 0.469388

-0.00243 0.00105 -2.32667**

0.000464 0.0093 0.498925

-0.029359 0.01212 -2.42236***

-0.019516 0.01322 -1.47625

-0.025262 0.0119 -2.12286**

-0.00496 0.00118 -4.20169***

-0.005770 0.0012 -4.80833***

-0.00249 0.00098 -2.53673***

103


perception of economic players and a mounting level of

risk in the banking sector do not determine the short-run

dynamics of bank lending under favourable economic

conditions. This implies that the affect of risk behaviour

is non-linear when monetary policy is tight or that risk

behaviour will only determine the short-term dynamics of

bank lending when exceeding a specific threshold (Li and

St-Amant, 2010). Further research is required to explain

this phenomenon.

Regarding the dynamics of working capital credit,

the impact of risk in the banking sector is significant,

however, risk perception is not influential when monetary

policy stance is loose or tight. This is possible because

working capital credit represents a long-term relationship

between the bank and its customers. Therefore, there is a

banking relationship in the case of working capital credit

that causes the banks’ perception of risk concerning their

customers to not greatly determine the allocation of bank

lending. Conversely, the level of risk in the banking sector

does determine the short-term dynamics of working capital

credit. This shows that the supply of credit declines when

an economy is languid (loose monetary policy), while

higher bank lending rates will reduce demand for working

capital credit when monetary policy is tight.

Holistically, based on the findings and empirical

analyses, the level of risk in the banking sector (which

interacts with monetary policy stance) has a significant

impact on the short-term dynamics of bank lending when

monetary policy is loose. Conversely, when monetary

policy is tight the level of risk in the banking sector is only

significant for working capital credit. The risk perception

of players in the banking sector is not significant for all

types of credit when policy stance is tight, but becomes

significant for investment credit and consumption credit

when policy stance is tight.

4.2. Impact of Monetary Policy Stance on the

Short-Run Dynamics of Credit

Based on the results presented in Table 4.1, the

impact of monetary policy stance on risk behaviour is as

follows:

InvestmentCredit

CapitalCredit

ConsumptionCredit

0.004322 -0.00789 -0.00548(0.00824) (0.00879) (0.00816)

-0.01786 -0.02453 -0.02876(0.00945)* (0.00993)*** (0.00929)***

The results presented in Table 4.1 show that only a

loose monetary policy stance has a significant impact on

the short-term dynamics of bank lending. Based on the

sign of coefficient, the impact of loose monetary stance on

bank lending is lower compared to when monetary policy

is not loose. Investment credit growth when monetary

policy is loose is 0.01786 (1.786%) lower than average

growth when the stance is not loose. This denotes that

if average credit growth is 10% when monetary policy

is not loose, then average investment credit growth will

be 8.214% when loose monetary policy is instituted.

Furthermore, average working capital credit growth is

2.453% lower when monetary policy stance is loose.

Finally, average consumption credit growth is 2.876%

lower when monetary policy stance is loose.

The implication of these empirical findings for

the impact of tight monetary policy is that monetary

contraction upon interaction with the two variables of risk

Source:

results of processed data. *** significant at 1%, ** significant at 5%,

*significant at 10%

Note:

Values in parenthesis are the standard error for the impact of monetary policy

stance (using the average value of the sample period for each risk variable).

Table 4.1Impact of Monetary Policy Stance

on the Short-Term Dynamics of Credit

104


behaviour does not determine the short-term dynamics

of bank lending for the three types of bank loans tested.

These empirical findings are not congruent with the

theory that the impact of monetary policy interacting

with risk behaviour will significantly determine the short-

run dynamics of bank lending. The findings indicate that

risk eliminates the role of monetary policy in economic

contraction. Contractive monetary policy is introduced

when an economy is booming, while risk perception

and risk in the banking sector are concomitantly easing.

Consequently, tighter monetary policy, which should

restrict economic growth through the credit channel,

becomes ineffective. These findings indicate that although

a central bank will institute tight monetary policy, it is

not tight enough to contract the economy through a

slowdown in bank lending.

Furthermore, when loose monetary policy interacts

with risk behaviour it has a negative and significant

impact on all three types of bank loan. These empirical

findings are in harmony with the theory that evidences a

significant impact from loose monetary policy interacting

with risk behaviour, the coefficient of which is negative.

This indicates that expansive monetary policy can catalyse

bank lending. During expansive monetary policy (designed

to stimulate a sluggish economy), risk perception and

risk in the banking sector tend to be high (on average

for the analysis period). Consequently, looser monetary

policy does not stimulate economic growth through

increased bank lending, in fact the opposite is true and

bank lending tends to diminish, which leads to economic

contraction. These findings show that when a central bank

takes loose monetary policy in an attempt to drive the

economy, risk perception actual increases in the banking

sector. Therefore, banks set a high risk premium. In other

words, lending rate rigidity occurs when an expansionary

monetary policy stance is taken. Another explanation is the

propensity of players in the banking sector to see loose

monetary policy as an indication of sluggish economic

growth. Consequently, banks become more selective in

their allocation of assets to the credit sector.

V. CONCLUSION AND IMPLICATIONS

The most salient conclusion that can be drawn from

this research is that the risk perception of economic players,

along with the level of risk in the banking industry, plays

a significant role in monetary policy transmission through

the credit channel in Indonesia. The variables of risk

perception and level of risk in the banking industry trigger

a reversal in the desired impact of loose monetary policy.

A loose monetary policy stance is a signal to the banking

community of sluggish economic conditions. Conversely,

a tight monetary policy stance, taken to cool down the

economy through the bank-lending channel, is ineffective

upon interaction with the variables of risk perception and

risk level in the banking sector. This is possibly due to risk

behaviour that eliminates the role of monetary policy in

terms of contracting the economy.

Indirectly, this finding also implies that in the case

of Indonesia, a loose monetary policy stance causes

the banking sector to become more risk averse, which

contradicts the findings of Taylor (2009) in the case of

advanced countries, where the banking sectors in such

countries became risk takers when looser monetary policy

was implemented. Analysis also showed indications of

asymmetric effects from loose and tight policy stance

upon interaction with risk variables. When monetary

policy is tight, risk behaviour tends to eliminate its impact

on short-term bank lending. Oppositely, when monetary

policy stance is loose, a decline in the central bank’s policy

rate does not precipitate a corresponding decline in bank

lending rates that would catalyse bank lending growth,

as an effect of risk behaviour that tends to be high when

economic conditions are sluggish.

105


A number of basic policy implications can be drawn

from this research that demonstrate a requirement for

integration between monetary and macroprudential policy

in the financial sector, as follows. First, Bank Indonesia

is required, as the monetary and banking authority, to

calculate the role of risk perception in the banking sector

when formulating monetary and financial system policy in

Indonesia. This research can form the basis of calculating

risk behaviour in the financial sector in terms of the

reaction function of monetary policy, which is broadly

expected to overcome economic (pro)cyclicality. Second,

in the context of deepening and expanding the role of the

financial market, Bank Indonesia must conducted more in-

depth studies into the impact of financial sector dynamics

on the efficacy of monetary policy. Third, the relevant

authorities are required to strengthen integrated policy

coordination in terms of instituting monetary policy in line

with close interaction between the dynamics (stability) of

the monetary sector and financial sector.

Finally, it is important to note that this research is

preliminary. Analytically, this research has only uncovered

evidence of the risk channel in the monetary policy

transmission mechanism in Indonesia, at least when

monetary policy is loose. Excluding this fact, black-box

characteristics persist, considering how the process and

channel of monetary policy transmission through the risk

channel can still not be fully explained by these findings.

Therefore, looking ahead more in-depth research is

required to observe the black box phenomenon. In

addition, the methodology could be further refined, in

particular by using alternative indicators of risk behaviour,

disaggregating the assessments of risk behaviour indicators

referring to individual banks and bank groups, as well as

observing the possible presence of feedback mechanisms

between the two variables of risk used. These refinements

are expected to answer a number of outstanding empirical

questions that remain unanswered by this research.

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107

Article II. Towards Stronger ASEAN Financial System:A Proposal for Regional Financial Stability Framework

Article 2

Towards Stronger ASEAN Financial System:A Proposal for Regional Financial Stability Framework1

Cicilia A. Harun2, Elis Deriantino3

1. INTRODUCTION

108


2. EAST ASIAN FINANCIAL STRUCTURE AND

REGULATIONS

109


3. SOME ISSUES IN SYSTEMIC RISK

SURVEILLANCE: LESSONS FROM THE EURO ZONE

CASE

110


Table A2.1Possible Patterns of Vulnerabilities and

Conflict of Interests

Home Country/ Parent Bank

Systemic

Non-Systemic

Systemic Non-Systemic

Figure A2.1Gross External Asset and Liability of

ASEAN Countries

2001 2002 2003 2004 2005 2006 2007 2008 2009

111


4. ISSUES IN COMBINING MICRO AND MACRO-

PRUDENTIAL SURVEILLANCE FOR CRISIS

PREVENTION

The Top-down and Bottom-up Approach in

Macroprudential Surveillance

Establishing A Level Playing Field

112


5. THE REGIONAL FINANCIAL SAFETY NET

(CRISIS RESOLUTION FRAMEWORK)

113


Table A2.2General and Specific Burden Sharing in Cross Border

Crisis Resolution

General Burden Sharing Specific Burden Sharing

6. CONCLUSION: THE PROPOSAL FOR ASEAN

FINANCIAL STABILITY FRAMEWORK

114


Figure A2.2Proposed ASEAN Financial Stability Framework

Sound and Credible RegionalFinancial System

Strong

National

Financial

Stability

Framework

Inclusive

Periodical

Regional

Financial

Stability

Report

Cooperative

College of

Supervisory

and

Regulatory

Authorities

Reliable

Regional

Financial

Safety Net

Sound and reliable regional network of payment systems and securities exchanges

115


116


References

117


DIRECTOR

Wimboh Santoso Suhaedi Linda Maulidina

COORDINATOR & EDITOR

Dwityapoetra S. Besar Iman Gunadi

WRITERS

Agusman, Ardiansyah, Anto Prabowo, Ratih A. Sekaryuni, Pungky Purnomo, Imansyah, Wini

Purwanti, Boyke Wibowo Suadi, Henry R. Hamid, Bambang Arianto, Ita Rulina, Iman

Gunadi, Wahyu Hidayat, Noviati, Januar Hafidz, Cicilia A. Harun, Sagita Rachmanira,

Reska Prasetya, Heny Sulistyaningsih, Mestika Widantri, Elis Deriantino, Hero Wonida,

Primitiva Febriarti, M. Ardian, Herriman Budi Subangun, Khairani Syafitri, Advis Budiman,

Harris DP, Louvti Sidabalok, dan Dyta Tri Utami

COMPILATOR, LAYOUT & PRODUCTION

Dwityapoetra S. Besar Primitiva Febriarti Ratih Maharani

CONTRIBUTOR

Directorate of Banking Supervision 1



Directorate of Sharia Banking

Directorate of Credit, Rural Bank Supervision and SMEs

Directorate of Bank Licensing and Banking Information

Directorate of Accounting and Payment Systems

Directorate of Reserve Management

Directorate of Economic Reseach and Monetary Policy

DATA SUPPORT

Suharso I Made Yogi

Financial Stability Review

No. 16, March 2011

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