ANALISIS TRIULANAN Peremangan Moneter Peranan … · Dr. Darsono Dr. Siti Astiyah Dr. Andi M....

1ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007

BULLETIN OF MONETARY ECONOMICS AND BANKING

Central Banking Research DepartmentBank Indonesia

PatronCentral Banking Research Department

Board of Editor

Prof. Dr. Anwar NasutionProf. Dr. Miranda S. Goeltom

Prof. Dr. InsukindroProf. Dr. Iwan Jaya Azis

Prof. Iftekhar HasanProf. Dr. Masaaki Komatsu

Dr. M. SyamsuddinDr. Perry Warjiyo

Dr. Iskandar Simorangkir Dr. Solikin M. JuhroDr. Haris Munandar

Dr. Andi M. Alfian ParewangiDr. M. Edhie Purnawan

Dr. Burhanuddin AbdullahDr. Andi M. Alfian Parewangi

Editorial Chairman

Dr. Perry Warjiyo

Managing EditorDr. Darsono

Dr. Siti AstiyahDr. Andi M. Alfian Parewangi

SecretariatIr. Triatmo Doriyanto, M.S

Nurhemi, S.E., M.ATri Subandoro, S.E

This bulletin is published by Bank Indonesia, Central Banking Research Department. Contents and results research in the writings in this bulletin entirely the responsibility of the authors and not an official view of Bank Indonesia.

We invite all parties to write in this bulletin paper delivered in the form files to Central Banking Research Department, Bank Indonesia, Tower Sjafruddin Prawiranegara Floor 21; Jl. M.H. Thamrin No. 2, Central Jakarta, email: [email protected]

The Bulletin is published quarterly in April, July, October and January.

Quarterly Outlook on Monetary, Banking, and Payment System in Indonesia:

Quarter I, 2016

TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman,

Tri Kurnia Ayu K, Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi

Central Bank Policy Mix: Key Concepts and Indonesia’s Experience

Perry Warjiyo

Impact of Financial Inclusion on Financial System Stability in Asia

Azka Azifah Dienillah, Lukytawati Anggraeni

Macroeconomics Indicators and Bank Stability: A Case Of Banking in Indonesia

Norzitah Abdul Karim, Syed Musa Syed Jaafar Al-Habshi, Muhamad Abduh

Interest Rate Metric System: Alternative Strategy for Banking Industry

Stephanus Ivan Goenawan

BULLETIN of moNETary EcoNomIcsaNd BaNkINg

Volume 18, Number 4, April 2016

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337Quarterly Outlook on Monetary, Banking, and Payment System in Indonesia: Quarter I, 2016

QUARTERLY OUTLOOK ON MONETARY, BANKING, AND PAYMENT SYSTEM IN INDONESIA:

QUARTER I, 2016

TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman, Tri Kurnia Ayu K,

Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi1

1 Authors are researcher on Monetary and Economic Policy Department (DKEM). TM_Arief Machmud ([email protected]); Syachman Perdymer ([email protected]); Muslimin AAnwar ([email protected]); Nurkholisoh Ibnu Aman ([email protected]); Tri Kurnia Ayu K ([email protected]); Anggita Cinditya Mutiara K ([email protected]); Illinia Ayudhia Riyadi ([email protected]).

The growth of domestic economy in Indonesia is lower than forecasted in first quarter of 2016.

However, the economy is expected to revive and will grow higher in the next quarter, with a well maintained

financial system stability. The limited growth of government consumption as well as private investment are

the main reason for the slower growth in this quarter, eventhough the government spending on capital

goods accelerates. The growth of private consumption remains high with reasonable price movement.

With the increase of several commodities’ export, the external performance of export in aggregate also

increased. On the other hand, the financial system stability was stable due to viable banking system and

better financial market performance. The stability of Rupiah was well maintained, supported by positive

expectation on domestic economy and the lower risk of the global financial market.

Abstract

Keywords: Macroeconomy, Monetary, Economic Outlook

JEL Classification: C53, E66, F01, F41

338 Bulletin of Monetary, Economics and Banking, Volume 18, Number 4, April 2016

I. GLOBAL DEVELOPMENT

The global economy is expected to grow at a slower pace by 2016. The US economic recovery is still not solid, as indicated by weak consumption and some employment indicators, as well as low inflation. This condition is expected to encourage the Fed to remain cautious in adjusting the Fed Fund Rate (FFR) rate. Correspondingly, European economic growth is also limited and is overshadowed by the Brexit issue. Meanwhile, Japan’s economy is still depressed. These conditions encourage further easing of monetary policy in developed countries, including through the adoption of negative interest rates. On the other hand, China’s economy is starting to improve, although it is still at risk, sustained by the construction sector and real estate. In commodity markets, world oil prices are expected to remain low, due to high supply amid weak demand. However, the prices of some Indonesian export commodities are improving, such as CPO, tin, and rubber.

The US economic recovery is still not solid, as indicated by weak consumption and some employment indicators, as well as low inflation. Weak consumption is reflected in the declining personal consumption expenditure (PCE) (Graph 1). In terms of labor, the lack of solid US economic recovery is indicated by a decrease in US employment, reflected in the slowdown in nonfarm payrolls and rising unemployment rates. In addition, the inflation rate is still low, including PCE Headline and PCE Core Inflation which is also slowing. This is partly influenced by falling food and energy prices (Graph 2).

Still not solid US economic recovery is expected to encourage the Fed to remain cautious in making adjustments to the Fed Fund Rate (FFR) rate. However, the risk of an increase in FFR remains to be wary of. FOMC Release minutes in April 2016 are more ‘hawkish’ than expected, with an increased chance of rising FFR June 2016. Additionally, the same Fed Office Statement lately reinforces the risks of FFR increases in June 2016.

Graph 1.Real Growth Personal Consumption Expenditure

Graph 2.The Growth of US Inflation

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Correspondingly, European economic growth is also limited and overshadowed by the referendum of UK membership in the EU on 23 June 2016 (issue of Brexit). The industrial sector grew moderately but in the downward trend (Graph 3). Export growth is also still in a downward trend, although in recent developments experienced a rebound. In addition, the issue of Brexit is also a source of new uncertainty that could increase the volatility of global financial markets and worsen the economy of the UK and the Euro Region. Brexit could have a negative impact on investors’ and consumer confidence in Europe that is currently down.

On the other hand, China’s economy is starting to improve, although it is still at risk, sustained by the construction sector and real estate. China grew at an estimated 6.7% (yoy) in Q1 2016, driven by construction and real estate. The growth of construction sector and real estate exceeded other sectors by 7.8% and 9.1%, respectively. Meanwhile, Fixed Asset Investment (FAI) accelerated in March 16 to 10.7% (yoy) (Graph 4). Lending is growing strongly in the corporate and household sectors. However, strong consumption roles and some other weak indicators (including retail sales and inventory) indicate China’s economic recovery is still at risk.

Graph 3.Industrial Production Development

Graph 4.The Development ofChina’s Fixed Asset Investment

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Meanwhile, Japan’s economy is still depressed. Consumption is still worrying, reflected in household spending showing a slowdown. In addition, exports and imports are still in negative territory in line with weak demand both globally and domestically. With these developments, Japan’s GDP and inflation are expected to be lower, in line with the BoJ’s move to revise down its GDP and CPI projections for 2016 and 2017 (Table 1).

In commodity markets, world oil prices are expected to remain low, due to high supply amid weak demand (Graph 5). The publication of various projections of world oil prices for 2016 and 2017 tends to be lower than the previous quarter estimate. Nevertheless, oil prices have risen in the last 3 months, due to falling US production, disruptions to Kuwait and Nigeria’s


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Graph 5.The Development of Brent Oil Price

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production, and weakening USD. The balance between world oil demand and supply is expected to be reached by the end of 2017.

Indonesia’s export commodity prices (IHKEI) have improved, although still low. The improvement of IHKEI is influenced by the price of CPO, tin, and rubber. The improvement in CPO prices was driven by production depressed by La Nina in June-August 2016 which caused price increases in the first quarter of 2016. Tin prices also rose, influenced by Chinese demand in line with increased construction activity. Meanwhile, rubber prices increased due to supply disruptions and increased prices of substitutes.

II. MACRO ECONOMIC DYNAMICS OF INDONESIA

2.1. Economic Growth

Domestic economic growth in the first quarter of 2016 was lower than expected and expected to improve in the next quarter. Growth in Q1 / 2016 was recorded at 4.92% (yoy), due to the limited growth in government consumption and private investment (Table 2), amid accelerated government capital expenditures. Meanwhile, household consumption is still growing quite strongly, supported by the maintained price developments. Externally, overall export performance also improved as commodity exports increased.


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Lower economic growth in Q1 / 2016, among others, was due to limited government consumption. Government consumption decreased compared to the previous quarter’s growth of 7.31% (yoy). The decline was influenced by the seasonal pattern of government spending at the beginning of the year is still relatively limited.

In addition to government consumption, slowing economic growth is affected by limited investment improvements. Overall, investment grew to slow to 5.57% (yoy) from 6.90% (yoy) in the fourth quarter of 2015. This growth was mainly driven by the non-construction investment kontontraksinya. Based on its kind, non-construction investment recorded a contraction of 0.26% (yoy) compared with a positive growth of 3.10% (yoy) in Q4 2015. This was influenced by the decline in machinery and equipment investment, in line with the still-capital goods import. Meanwhile, building investment grew slightly slower from 8.21% (yoy) to 7.67% (yoy) in the first quarter of 2016. Slowing construction investment was mainly due to limited private investment buildings amid accelerated government infrastructure projects. This condition is reflected in weakened cement sales (Graph 6).

Meanwhile, household consumption grew strongly supported by sustained price developments. Household consumption contributed to economic growth in Q1 / 2016. This was reflected in the relatively stable growth in household consumption of 4.97% (yoy) from 4.95% (yoy). The strength of household consumption is driven by increases in non-food consumption, particularly transportation consumption, in line with lower fuel prices, and consumption of communications. Strong household consumption is supported by a number of consumption indicators that show positive developments. Retail sales are increasingly sourced from improvements in the sales of communications groups and household appliances. In line with the positive retail sales, improvement in motorcycle sales continued in Q1 / 2016. In addition, the Consumer Confidence Index (IKK) in the first quarter of 2016 also showed improvement (Graph 7).


Graph 6.Cement Sales

Graph 7.Consumer Confidence Index

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Externally, overall export performance also improved, although still contracted, in line with increased exports of some commodities. Exports in Q1 / 2016 recorded a 3.88% (yoy) contraction, improved compared to the previous quarter’s 6.44% (yoy) contraction. Based on the group, the suspension of export contraction was supported by improvements in manufacturing exports (Graph 1.20). Manufacturing exports rose supported by improvements in export performance of basic metals, electrical appliances, and processed wood. Meanwhile, agricultural exports grew relatively stable driven by positive exports of fish. On the other hand, mining exports are worsening driven by deeper contraction in coal exports due to continued economic slowdown in China.

Responding to household consumption is quite strong and began to squirm the manufacturing sector, imports have improved although still contracted. The contraction in imports improved in Q1 / 2016 to 4.24% (yoy) from 8.05% (yoy) in the fourth quarter of 2015. The holding of import contraction was mainly supported by improving imports of consumer goods and raw materials (Graph 1.21). Consumer goods imports grew positively, driven by increasing imports of durable and semi durable consumer goods. Meanwhile, the contraction in raw material imports improved mainly due to the rise in imports of primary food and beverages for industry, fuel and components and accessories for capital goods. In line with the weakness of non-construction investment, imports of capital goods are still experiencing increasingly contraction in imports of capital goods except for transportation.

By sector (business field), the economic slowdown is mainly influenced by the weakening performance of some non-tradable sectors. The slowing down of the non-tradable sector is mainly influenced by the performance of the financial services sector, construction sector, and the slower information and communication sector (Table 1.3). The financial services sector slowed due to the low lending rate. Meanwhile, the slowdown in the construction sector


is related to limited construction activity by private investors, amid the progress of several government infrastructure projects. On the other hand, the slowdown in the information and communication sector is due to the limited completion of 4G network penetration in large cities. Despite the nominal increase in growth but the increase in telecommunications revenues is still based on the increase in basic internet package price (Rp / kbps). In terms of tradable sectors, the growth of the manufacturing sector sector has increased in line with the performance of manufacturing exports which are also improving. The increase is also in line with the rise in the PMI in March 2016 to a level above 50. In addition, the realization of government spending through infrastructure also creates incentives in the manufacturing sector, especially in the heavy equipment sub-industry. Similarly, the automotive performance began to improve as reflected in improved motorcycle sales.

On the spatial side, the economic slowdown in Q1 / 2016 occurred in almost all parts of Indonesia, mainly contributed by the economic slowdown in Java. Economic growth in the Java region slowed from 5.87% (yoy) in the fourth quarter of 2015 to 5.31% (yoy), mainly affected by the limited absorption of government spending in various regions of Java. The economic slowdown also occurred in Sumatra from 4.56% (yoy) in the fourth quarter of 2015 to 4.18% (yoy), influenced by the decrease of oil palm production due to high rainfall. Kalimantan’s economy grew 1.08% (yoy), lower than the previous quarter which grew by 1.45% (yoy). Continued decline in coal exports as China’s slowing economy is one of the reasons for

Figure 1. Regional Economic Growth Map of Quarter I 2016

ACEH3.7

SUMUT5

SUMBAR5.5

RIAU2.3

JAMBI3.4

SUMSEL4.9

BENGKULU5

LAMPUNG5.1

KEP. RIAU4.6

KEP. BABEL3.3

BANTEN5.1

DKI JAKARTA5.6

JABAR5.1

JATENG5.1

DIY5 JATIM

5.3

BALI6

KALBAR5.9

KALTENG5.2

KALSEL4

KALTIM-1.29

NTB10

NTT5.1

SULUT6

GORONTALO6.6

SULTENG11.8

SULSEL7.4

SULBAR6.1

SULTRA5.2

MALUT5.1

MALUKU5.5

PAPBAR5.5 PAPUA

-2.03

5.0% < PDRB < 6.0 PDRB < 0%0% < PDRB < 4.0%

Realisasai PDB Nasional TW’16: 4.92%

4.0% < PDRB < 5.0%6.0% < PDRB < 7.0%PDRB > 7.0%

3.5

I II III IV I

3.0 3.1

4.6 4.2

2015

Sumatera

2016

5.3

I II III IV I

5.25.5

5.9

5.3

2015

Jawa

2016

2.0

I II III IV I

1.4

0.4

1.51.1

2015

Kalimantan

2016

6.5

I II III IV I

9.4 8.9 8.6

6.0

2015

KTI

2016


the economic slowdown in Kalimantan. The decline in export performance caused the East Kalimantan economy to contract deeper from the previous quarter. Similarly, the economy of eastern Indonesia (KTI), which grew from 8.60% to 6.01% in the first quarter of 2016. The slowdown in the KTI region was mainly driven by the contraction of economic growth in Papua Province due to the decrease of mineral production.

2.2. Indonesia’s Balance of Payments

The current account deficit in Q1 / 2016 declined, driven mainly by an increase in the trade sur plus. The current account deficit fell from 5.1 billion dollars (2.4% of GDP) in the fourth quarter of 2015 to 4.7 billion US dollars (2.1% GDP) in the first quarter of 2016 (Graph 8). The decrease in current account deficit was mainly supported by the surplus of non-oil and gas trade balance which increased due to the decrease of non-oil / gas imports (-5.2% qtq) which was larger than the decrease of non-oil and gas exports (-2.6% qtq). This is in line with the limited domestic demand. Meanwhile, although overall non-oil / non-gas exports declined, the performance of exports of some non-oil and gas commodities began to show improvement. On the oil and gas side, the oil and gas trade balance has improved in line with the shrinking oil imports due to lower world oil prices. The improvement in current account performance was also contributed by the decrease in the service account deficit following the decline in imports of goods and the decline in national tourist spending during the visit abroad. Meanwhile, the deficit in primary income balance has increased related to the pattern of interest payment of government bonds.

Graph 8.Current Transaction Deficit (% GDP)

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In the meantime, capital and financial transactions in Q1 / 2016 recorded a surplus in line with improving domestic economic outlook and continued easing of monetary policy in developed countries. The capital and financial account surplus in Q1 / 2016 reached 4.2 billion US dollars, mainly supported by portfolio investment inflows and direct investment inflows. The net capital portfolio investment inflows continued to grow and reached 4.4 billion US dollars for the entire first quarter of 2016. The portfolio investment portfolio came from the government’s global sukuk, rupiah-denominated government securities and stocks. Direct investment also recorded a surplus of 2.2 billion US dollars, although smaller than the surplus in the fourth quarter of 2015 amounted to 2.8 billion US dollars. In total, the capital and financial account surplus in Q1 / 2016 was lower than the previous quarter surplus. This is mainly due to other deficit-related investments as a result of the low level of withdrawal of private foreign loans.

Overall, Indonesia’s balance of payments (NPI) in the first quarter of 2016 experienced a deficit in line with lower capital and financial account surplus. The NPI deficit was recorded at 0.3 billion US dollars (Graph 9). The foreign exchange reserves at the end of March 2016 was recorded at 107.5 billion US dollars. The amount of foreign exchange reserves is sufficient to finance the payment needs of imports and government foreign debt for 7.7 months and is above the international standard of adequacy.

The position of Indonesia’s foreign reserves at the end of April 2016 was recorded at 107.7 billion US dollars, higher than the end of March 2016 of 107.5 billion US dollars (Graph 10). The increase is influenced by the receipt of foreign exchange reserves, primarily derived from the results of Bank Indonesia Securities (SBBI) auctions and other receipts. Such revenues exceed the foreign exchange requirement, which is used in part to pay the government’s foreign debt. The position of foreign reserves at the end of April 2016 is sufficient to finance 8.1 months of imports or 7.8 months of imports and servicing of official external debt, and is

Graph 9.Indonesia’s Balance of Payments

Graph 10.The Development of Foreign Exchange Reserves

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Graph 11.Rupiah Exchange Rate

Graph 12.Regional Exchange Rate

above the international standard of approximately 3 months of imports. Bank Indonesia believes that the foreign exchange reserves are able to support the resilience of the external sector and maintain the sustainability of Indonesia’s economic growth in the future.

2.3. Rupiah Exchange Rate

The stability of the rupiah was maintained. During Q1 / 2016, the rupiah exchange rate, point to point (ptp), rose 3.96% and reached the level of Rp13,260 per US dollar (Graph 11). The strengthening of the rupiah in Q1 / 2016 was driven by continued foreign capital inflows in line with optimism over the outlook for the domestic economy and the maintenance of external risk factors. The strengthening of the rupiah in the first quarter of 2016 was driven by domestic and external factors. From the domestic side, the continued strengthening of the rupiah is supported by positive perceptions of the domestic economy due to the maintenance of macroeconomic stability and optimism for future economic growth. This is in line with the decline in the BI Rate and government policy packages to improve the investment climate, and accelerate the implementation of infrastructure projects. In addition, the strengthening of the rupiah was also supported by domestic export-oriented corporate foreign exchange supply. From the external side, the strengthening of the rupiah is driven by the easing of risks in global financial markets related to the rise in FFR and the continued easing of monetary policy in some developed countries. The movement of the rupiah is accompanied by maintained volatility. In the first quarter of 2016, rupiah exchange rate volatility recorded a decline and was relatively lower than that of some peers. This is in line with the strengthening of the rupiah exchange rate that occurred gradually since February 2016 (Graph 12).

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2.4. Inflation

Inflation is at a low level and is expected to remain within the 2016 inflation target range of 4 ± 1%. In Q1 / 2016, the Consumer Price Index (CPI) recorded deflation of 0.62% (qtq) or inflation of 4.45% (yoy), down from the previous quarter which recorded inflation of 1.27% (qtq) or 6, 87% (yoy). The inflationary decline came from administered prices (AP) and volatile foods (VF), while core inflation was relatively stable (Graph 13).

Core inflation in Q1 / 2016 was driven by subdued inflation expectations. Core inflation was recorded at 0.80% (qtq) or 3.50% (yoy), relatively stable from the previous quarter’s inflation of 0.62% (qtq). Tend to stabilize core inflation in Q1 / 2016 driven by strengthening of Rupiah and subdued inflation expectation. Consolidated core inflation was also supported by

Graph 13.Inflation Growth

Graph 14.Expectations on Retailers

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Graph 15.Consumer Inflation Expectation


Figure 2. CPI Inflation Stock Map (%, mtm)

inflation expectations in declining retail and consumer levels. However, in the short run, inflation expectations show little improvement, especially entering the middle of the year according to the pattern associated with the new school year, Ramadhan, and Eid al-Fitr. The increase is reflected in inflation expectations at the retail and consumer level (Graph 14 and Graph 15)

In quarter I 2016, on a quarterly basis (qtq), the volatile foods category recorded inflation of 2.47%, lower than volatile foods inflation in the previous quarter of 2.62%. Lower volatile foods inflation in Q1 2016 is mainly driven by commodities of chicken meat and eggs in line with the increasing supply of day old chick and animal feed.

In quarter I-2016, on quarterly basis (qtq), administered prices category in Tw I 2016 recorded deflation of 1.64%, lower than inflation in administered prices at Tw IV 2015 of 1.09%. The deflation of the administered prices category in Q1 2016 was mainly driven by the decline in fuel prices and the strengthening of the rupiah.

Spatially, inflation in March 2016 (mtm) is relatively low occurred in Eastern Indonesia (KTI), Kalimantan, and Java. Inflation in Kalimantan and Java was 0.10% (mtm) and 0.16% (mtm), respectively, lower than national inflation (0.19%, mtm). Meanwhile, the highest inflation occurred in Sumatra, mainly contributed by North Sumatra and West Sumatra. This, among others, is associated with an increase in the price of shallots and red peppers (Figure 2).

ACEH-0,21

SUMUT0,84

SUMBAR0,62

RIAU0,47

JAMBI0,2

SUMSEL0,26

BENGKULU0,04

LAMPUNG0,44

KEP. RIAU0,27

KEP. BABEL-0,27

BANTEN0,1

DKI JAKARTA0,15

JABAR0,2

JATENG0,38

DIY0,02 JATIM

0,04

BALI0,19

KALBAR-0,06

KALTENG-0,15

KALSEL0,14

KALTIM0,21

NTB-0,07

NTT-0,76

SULUT-0,03

GORONTALO0,15

SULTENG0,38

SULSEL0,03

SULBAR-0,02

SULTRA0,16

MALUT0,28

MALUKU-1,26

PAPBAR-0,07 PAPUA

0,11

1% < Inf < 2% Inf < 0%0% < Inf < 0,5%0,5% < Inf < 1%2,0%< Inf < 3,0%Inf > 3,0%


III. MONETARY DEVELOPMENT, BANKING AND PAYMENT SYSTEM

3.1. Monetary

Transmission of monetary policy easing, through the interest rate channel, begins to run. The monetary policy utilizing the easing space by lowering the BI Rate to 6.75% with the Deposit Facility (DF) rate to 4.75% and the Lending Facility (LF) to 7.25% in March 20162 RDG has been followed by a decrease in interest rates Operational target of O / N interbank money market.

The Interbank Money Market conditions in Q1 / 2016 were characterized by more flexible liquidity. The weighted average O / N interbank money market interest rate in Q1 / 2016 decreased from 6.00% in Q4 / 2015 to 5.26% in Q1 (Graph 16). In terms of timeframe, the weighted average of interbank money market rates with longer tenor decreases more than the interest rate with the O / N tenor, this is in line with the still high daily surplus liquidity. In the first quarter of 2016, bank placements in DF instruments declined to Rp73.26 trillion from Rp79.21 trillion. The average spread of max. Min interest rates of Interbank O / Money Market in the first quarter of 2016 fell to 15 bps from 50 bps in the preceding quarter. In nominal terms, the average volume of the total interbank money market in the first quarter of 2016 fell to Rp12.06 trillion from Rp12.39 trillion in the preceding quarter. The decline in interbank money market volume was mainly contributed by the decline in the interbank money market volume of O / N from Rp7.60 trillion to Rp7.13 trillion. While in April 2016, the average interbank money market rate of O / N again fell by 15 bps to 4.85% compared to the previous month at 5.00%.

Bank deposit interest rates declined, responding to the stance of monetary policy easing. The weighted average interest rate (RRT) of deposits in the first quarter of 2016 fell by 37 bps to 7.57%. The decline in deposit rates varies considerably between tenors. The decline in deposit rates occurred in 1-month tenor of 12 months, with the largest decrease in 1-month and 12-month tenures of 54 bps and 28 bps to 7.06% and 8.19%, respectively. The decline in deposit rates with long tenors is relatively slow due to the longer maturity.

Bank lending rates in the first quarter of 2016 declined compared to the previous quarter, moving in line with the BI Rate cut. In the first quarter of 2016, lending rates fell by 13 bps to 12.70% in line with lower BI Rate and deposit rates. The decline in loan interest rates occurred in KMK and KI. Meanwhile, KK still recorded increased by 3 bps to 13.91% (Graph 17). With these developments, the spread of deposit and lending rates in Q1 2016 rose by 24 bps to 513 bps.

Growth Economic liquidity (M2) and M1 slowed down. In Q1 / 2016, M2 was recorded at 7.41% (yoy), slower than M2 growth in Q4 / 2015 of 8.95% (yoy). The slowdown in M2

2 The Bank Indonesia Board of Governors Meeting on 16-17 March 2016 decided to lower the BI Rate by 25 bps to 6.75%, with the Deposit Facility of 4.75% and the Lending Facility at 7.25%. In 2016, the Bank Indonesia policy mix will remain focused on maintaining macroeconomic stability and financial system while maintaining the momentum of economic growth. In the monetary field, the utilization of monetary easing space is measured by consistently maintaining macroeconomic stability and financial system. To improve the effectiveness of monetary policy transmission, the focus in the short-term ahead will be more emphasis on strengthening the operational framework through the application of a consistent monetary operation rate structure.


growth in Q1 / 2016 was driven mainly by a slowdown in quasi money growth and M1. M1 growth in the first quarter of 2016 was recorded at 11.18% (yoy), slowing compared to Q4 / 2015 by 12.00% (yoy). The slowing growth in M1 in Q1 / 2016 was driven by a slowing of currency outside banks.

Graph 16.Corridor of Monetary Operation Interest Rates

Graph 17.Loan Interest Rate: KMK, KI dan KK

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Based on the factors that influence, M2 slowdown is sourced both from NFA and NDA (Chart 1.45). The decline in NFA was boosted by the NFA’s decline in both Bank-owned and NFA-owned NFA banks. Meanwhile, the slowdown in the NDA was driven by slowing credit and NCG.

3.2. Banking Industry

Financial system stability is maintained, supported by the resilience of the banking system and improved financial market performance. The improvements were mainly due to increased liquidity and bank capital and improvements in financial markets. Future SSK conditions are still maintained to support the intermediation process which is expected to grow higher.

Loan growth in Q1 / 2016 slowed in line with limited economic growth. Credit growth in Q1 / 2016 was 8.71% (yoy), lower than the same quarter in the preceding year which grew by 11.28% (yoy) (Graph 18). Slowing credit growth amid easing monetary policy is related to lower demand in keeping with slowing economic growth and rising bank cautiousness in credit disbursement due to an increase in credit risk as reflected by NPL ratio. On a sectoral basis, the credit slump in Q1 / 2016 across all sectors has not improved. Only the credit of the Business Services sector grew higher than the previous quarter, while other sectors were still weak.


Third party funds (DPK) growth in March 2016 was recorded at 6.4% (yoy), down from 6.9% (yoy) in the previous month (Graph 1.49). The decline in (DPK) in March 2016 was mainly due to the decrease in time deposits due to lower interest rates in line with slowing revenues.

Graph 18.Kredit Growth by Use

Graph 19.Growth of DPK

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Sumber: LBU

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The banking condition is still well maintained amid slowing credit growth. In Q1 / 2016, capital adequacy was still adequate with a high Capital Adequacy Ratio (CAR) of 21.8%, well above the minimum requirement of 8% (Table 3). In line with the credit slowdown, credit risk (NPL) in Q1 / 2016 tended to show an increase from 2.49% in Q4 / 2015 to 2.83% in line with slowing global and domestic economic growth.


3.3. Stock Markets and State Securities Markets

The development of the domestic stock market during the first quarter of 2016 showed improved performance, driven in part by domestic positive sentiment as the release of economic fundamentals data began to show improvement. The performance of the JCI in Q1 / 2016 reached 4,845.37 (31 Mar 2016), up by 22 points or 5.5% (qtq) (Graph 20). JCI continues to show improvement over time during the period January to March 2016. This strengthening is influenced by domestic positive sentiment over the relatively stable release of relatively stable Indonesian macro stability indicator data as well as easing of exchange rate pressures. While on the global side, positive sentiment emerged from expectations of stalled FFR rise in the near future. JCI’s performance is good compared to regional stock market movements (Vietnam, Philippines, Malaysia and Singapore). JCI growth is large among regional countries and is below Thailand (9.3%), but still above Malaysia, the Philippines and Vietnam.

In line with the stock market, the SBN market showed a positive performance. Improved SBN market conditions are characterized by declining yield on government securities across all tenors. Overall, the yield fell by 110 bps to 7.73% in Q1 / 2016 from 8.84% in Q4 2015. The improvement was driven by global and domestic positive sentiment. Positive sentiment is derived from government policies that reduce fuel prices are expected to have a positive impact on inflation reduction. The short, medium and long term yields decreased by 123 bps, 117 bps and 79 bps to 7.37%, 7.71% and 8.28%, respectively. Meanwhile, the benchmark 10-year yield fell by 108 bps to 7.67% from 8.75%. Decrease in yield during the quarter of 2016 was influenced by the back of strengthening oil prices and falling expectations of FFR increases following the release of dovish FOMC results. On the domestic front, yield decline was influenced by positive sentiment over declining BI Rate, improved trade deficit and government policies that reduced fuel prices. In addition, investor interest in government securities is still

Graph 20.JCI and Global Stock Indeks, Quarter I 2016 (qtq)

Graph 21.SBN and Net Selling / Buying Foreign, Quarterly

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high, reflected in the government’s SBN auction which is still oversubscribed. Amid declining yield on government securities, nonresident investors posted a net buy of Rp47.53 trillion in Q1 / 2015, up from Rp34.06 trillion in the previous quarter (Graph 21).

3.4. Non-Bank Financing

Non-bank economic financing declined. Total financing during Q1 / 2016 through initial issue, rights issue, corporate bonds, medium term notes, promissory notes and other financial institutions decreased from Rp41.6 trillion in Q4 / 2015 to Rp24.2 trillion (Table 1.7). The decline was mainly driven by lower share issuance to Rp0.8 trillion from Rp29.1 trillion in the preceding quarter, in line with slowing economic growth and high volatility risks in the stock market. Thus, some investors tend to choose alternative financing through the issuance of bonds. In Q1 / 2016, bonds increased from Rp6.9 trillion in Q4 / 2015 to Rp17.8 trillion in Q1 2016.

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3.5. Payment System Development

The development of rupiah money management in general is in line with the development of the domestic economy, especially from the household consumption sector. UYD in Q1 / 2016 was Rp508.6 trillion, or grew 9.9% (yoy), down from the previous quarter of Rp586.8 trillion (11.0%, yoy) (Graph 22). The decrease in UYD was more due to seasonal pattern in the first quarter. The currency return from banks and the public to Bank Indonesia after the Christmas and late 2015 periods led to a decrease in UYD.


Amid the downward trend in UYD, Bank Indonesia continues to improve the quality of money in circulation. During Q1 / 2016, a total of 1.8 billion pieces of Inappropriate Money (UTLE) in various denominations worth Rp57.2 trillion have been destroyed and replaced with proper circulating rupiah. The number of UTLE destruction is higher compared to the fourth quarter of 2015, which was recorded at 1.7 billion shares or Rp44.0 trillion. The high rate of destruction in quarterly reports is in line with the currency flows of banks and communities to Bank Indonesia after the Christmas and late 2015 periods. Some of these inflows have unbecomable quality so that they are destroyed (the inflation rate to 35.2%).

The implementation of the payment system during the first quarter of 2016 runs safely, smoothly, and well maintained. Overall, non-cash payment system transaction volume increased in Q1 / 2016, driven mainly by an increase in APMK (Card-Based Payment Instrument) transactions. The increase in non-cash transaction volume reflects the widespread use of non-cash payment instruments by the public, especially ATM Cards and / or Debit Cards. Meanwhile, the volume of payment transactions held by BI (BI-RTGS, BI-SSSS, and SKNBI) was recorded at 30,877.25 thousand transactions, down 6.75% from 33,111.40 thousand transactions in the previous quarter. The decrease in transaction volume was due to lower transaction volume of the BI-RTGS System and SKNBI, respectively by 39.43% (qtq) and 4.29% (qtq) (Table 5).

The volume of APMK transactions in Q1 / 2016 recorded positive growth compared to the previous quarter. The volume of APMK transactions increased by 0.69% to 1,293,820.18 thousand transactions. ATM and / or Debit Card still dominate the volume and value of APMK transactions with a proportion of respectively 94.28% and 94.90%. In terms of value, APMK transactions decreased by 0.07% to Rp1,368.51 trillion derived from a decrease in Credit Card

Graph 22.UYD Developments (yoy)

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(Table 6). The decrease in the volume and value of credit card transactions during the quarter under review, as shown in table I and II, is a cyclical decrease considering that in the previous quarter there was an end of year holiday.

BI-RTGS system transactions decreased, both in terms of volume and value compared to the previous quarter. The volume of payment system transactions completed through the BI-RTGS system decreased by 39.43% to 1,436.25 thousand transactions. The decrease was followed by a decrease in transaction value by 3.60% (qtq) to Rp26,739.53 trillion in the first quarter of 2016. In general, the decline in transactions was due to a policy to increase the minimum transaction value limit through the BI-RTGS system to above Rp500 million, post implementation BI-RTGS Generasi II system in November 2015. It can be seen from the transfers of public funds (inter-customer) that experienced a significant decrease in volume and transaction value, decreased by 47.25% (qtq) and 14.77% (Qtq).

The increase in the value of SKNBI transactions is driven by increased credit clearing / fund transfer transactions. This is the result of the implementation of the nominal upper limit of SKNBI fund transfer policy and the nominal lower limit of funds transfer through the BI-RTGS3 System. Through the policy, the average nominal per credit clearing transactions during the reporting period also increased to Rp 35.70 million from Rp29.27 million in the previous period and the same period in the previous year was Rp19.38 million per transaction.

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3 The nominal limit of transactions through SKNBI, which was originally a maximum of Rp500 million to be unlimited, while the nominal limit of funds transfer through the BI-RTGS System which was originally at least Rp100 million increased to Rp500 million.


IV. ECONOMIC PROSPECTS

Bank Indonesia expects economic growth in 2016 to be higher than 2015 at 5.0 to 5.4%. Improved domestic economic performance is expected to be supported by increased investment and infrastructure spending as well as rising incomes which then encourage consumption to remain strong. However, this forecast is lower than the previous projection, mainly due to lower consumption and investment realization in Q1 / 2016, thus indicating weak domestic demand. On the other hand, exports are expected to be better than previous projections, mainly due to higher commodity prices assumptions and higher than expected export realizations in Q1 / 2016.

In the same period, inflation is expected to remain under control within its target range. The relatively stable inflation rate until April 2016 indicates that the BI and Government policies that have been pursued so far can control inflationary pressure from both demand and supply side. In addition, subdued inflation expectations, relative low international commodity prices, and a volatile exchange rate are expected to support inflationary moves towards their targets.

Bank Indonesia will continue to look at some of the risks that overshadow the process of economic adjustment in the future. From the global side, these risks, among others, are related to the not yet solid world economic growth and the plan to raise interest rates on US policy. From the domestic side, the risks that need attention are the limitations of fiscal space and economic financing.

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357Central Bank Policy Mix: Key Concepts and Indonesia’s Experience

CENTRAL BANK POLICY MIX:KEY CONCEPTS AND INDONESIA’S EXPERIENCE

Perry Warjiyo

The global crisis brings about renewed reforms on central bank policy. First, in addition to the

traditional mandate of price stability, there are strong supports for additional mandate of the central bank

to promote financial system stability. Second, macroprudential policy is needed to address procyclicality

and build-up systemic risks in the macro-financial linkages of financial system that in most cases precede

and deepen financial crisis. Third, monetary and financial stability are also prone to volatility of capital

flows, especially for the emerging countries, and thus there is a need to manage them. The challenge

is how to mix the policies of monetary, macroprudential, and capital flows management to meet the

renewed mandate of central bank on monetary and financial stability. This paper reviews theoretical

underpinnings and provides key concepts to address the issues. We show that central bank policy mix is

both conceptually coherent and practically implementable. We provide a concrete recommendation with

a reference from Indonesia’s experience since 2010. We also raise a number of challenges from practical

point of views, especially relating to decision making process, forecasting model, and communication,

for the success of the policy mix.

Abstract

Keywords: Central Bank, Monetary, Macroprudential, Capital Flows Management

JEL Classification: E58, E52, G28, F38

1 Deputy Governor, Bank Indonesia. Address: Jl. MH. Thamrin No. 2, Jakarta, Indonesia. E-mail: [email protected]; [email protected]. The views in this paper are of the author’s own personal and do not necessarily represent the official views of Bank Indonesia.


I. INTRODUCTION

The global crisis unveils a number of flaws in the economy that is based on capital financing intermediated through financial system. In the words of Minsky (1982), financial instability is inherent within capitalist economy whereby inflation and debt accumulation have the potential to spin out of control in the period of economic upswing. Stability is destabilizing, and thus leads to ‘boom’ and ‘bust’ in the financial cycles, causing the economy falls into crisis. That procyclicality of asset price bubbles and credit booms precedes and causes crises in many countries was not new (Claessens and Kose, 2013). Equally, that crisis is fundamentally a problem of excessive accumulation of debt, be it by public or private, has been found in long history of crises (Reinhart and Rogoff, 2009; Kindleberger, 1978). And that the crises are more frequent and metaphors into multifaceted financial crises of currency, debt, and banking in many countries are widely evidenced (Bordo, et.al, 2001).

What the global crisis have the implications on the central bank mandate and policy? First, beyond the primacy of price stability, central bank needs to have a key role in financial system stability. For one thing, monetary policy impacts stability of financial system through interest rate, exchange rate, firm’s decision on investment, bank lending, and investor portfolio decisions. Prolonged low interest rate under low inflation environment could elevate financial cycles and build-up systemic risks, and thus cause instability in the financial system and economy. The US experience clearly show this, whereby Great Moderation leads to housing bubbles, credit booms, excessive risk taking, and leverages. The Asian crisis of 1997-1998 shows similar case, whereby more than a decade of East Asian Miracle induced macro-financial imbalances that were then unveiled from the crisis: credit booms, property bubbles, and excessive private external borrowing. For the other thing, the stability of financial system is a key for effective monetary policy transmission. The recent experiences of advanced countries since the global crisis, notably in the US, Euro area, and Japan, show that the effectiveness of their ultra-quantitative easing and near zero interest rates has been constrained by deleveraging and restructuring process of their financial systems. The relation between monetary stability and financial stability is thus mutual, complimentary, and reinforcing.

Second, procyclicality and systemic risks in macro-financial linkages of the financial system could not be addressed by interest rate policy or microprudential measures. Monetary policy generally do not take sufficient account of build-up systemic risks from asset price bubbles or leverages, and assume that microprudential regulation could control such build up risks, of which is not the case (IMF, 2010). Monetary policy could “lean against the wind” to mitigate the asset price bubbles, e.g. by increasing interest when there is evidence of accelerated housing prices. But housing markets are driven mostly by factors beyond interest rates, particularly by buoyant expectation on further price increases and lackluster financing from banks, developers, or foreign borrowings. Interest rate increases also impact across the board to all sectors, not only to the housing market. Likewise, microprudential may be used to address the housing bubbles,


e.g. by increasing its risk weighted measures on capital requirement. But this is where another problem lies: risk valuation of capital requirement is by in itself procyclical. Risk valuation tends to underestimate the true risks during economic boom and overestimate during recession. For these conceptual and fundamental reasons, macroprudential policy gains wide supports as instrument to address procyclicality and systemic risks in the financial system, and that the central bank is the most appropriate institution to assume this function.

Third, volatility of capital flows to emerging countries has been excessively high since the global crisis. During the period from 2009 to mid-2013, large capital inflows to emerging market economies (EMEs) have been unprecedented, driven by huge global excess liquidity from ultra-quantitative monetary easing and near zero interest rates in the advanced countries searching for high returns. But the Fed tapper tantrum in May 2013 has changed their behaviors: excessively volatile and prone to risk-on and risk-off investors’ perception responding to short-term news. While their invaluable benefits for the economy are widely acknowledged, large capital flows, if not managed properly, can expose the EMEs to serious macro-financial imbalances and risks. Kawai and Takagi (2008) cited three types of risks emanated from volatile capital flows, i.e.: (i) macroeconomic risks of rapid credit growth, current account imbalances, and real exchange appreciation, (ii) financial instability risks of maturity and currency mismatches, asset prices increases, and decelerating quality of assets, and (iii) sudden stops risks and/or capital reversals of capital flows. Again, interest rate response alone would not be effective. While exchange rate flexibility is generally accepted response as shock absorber to external shocks, its excessive short-term volatility may pose serious risks to both monetary and financial stability. For these reasons, central banks in the EMEs adopts various measures of capital flows management to support their interest rate and exchange rate policies in achieving price stability and promoting financial stability.

The two main purposes of this paper are modest. First, we review growing thinking from the policy makers and academicians with a view to draw some common ground on the possibility of a policy mix on how central bank respond to these three challenges. Our focus is from the perspective of the EMEs. Key concepts will be discussed and outlined, including the mandate of central bank in financial stability, role and instruments of macroprudential policy, as well as capital flows management. Second, we present Bank Indonesia experience in formulating conceptual framework and implementing the policy mix since 2010. The policy framework is based on the inflation targeting using interest rate as the main instrument, complemented by exchange rate policy, capital flows management, and macroprudential measures. We find the policy mix plays an important role for Indonesia resilience in withstanding the bouts of uncertainty and volatility from the global economy and financial markets since the global crisis.

Next section of this paper outline the theory and concept of policy mix, the interest rate and exchange rate, financial stability, macroprudential policy, and the management of capital flow. Section three outline the method, while section four provide the result and analysis. Conclusion is presented section five and will close the presentation of this paper.


II. THEORY

2.1. Towards Central Bank Policy Mix

How central bank integrates monetary policy, macroprudential policy, and capital flows management in its policy mix to carry the dual mandates of achieving price stability and promoting financial stability? The already established monetary policy framework in the central banks provides strong basis for this. More than last two decades, the central banks have been successful in delivering price stability in many countries, both advanced and the EMEs. In part this reflects the intense sharing experiences around the close central bank community, and in other part it is supported by the credible adoption of inflation targeting framework. This framework has been successful in bringing down long-term trend of inflation, higher output, and declining interest rates in many countries (Berg, et. al., 2013). A number of key features of the framework that support the monetary policy credibility includes: clarity of inflation target to be achieved, rigorous macroeconomic forecasting and policy analysis models, consistency of the interest rate to achieve the target, independency of the central bank, formal and regular decision making process, publication of inflation forecast and other modes of communication to anchor inflation expectation. To carry the additional mandate of promoting financial stability, what needs to be done is to enlarge the framework by incoporating macro-financial linkages, particularly through the financial system and capital flows, into the macroeconomic forecasting and policy analysis. This will provides the basis for formulating the monetary response, as well as macroprudential policy and capital flows management that are needed to achieve price stability and promote financial stability.

2.2. Central Bank’s Mandate of Financial Stability

There has been now strong supports for the central banks to assume a role in promoting financial system stability (BIS, 2011). But what is financial stability? Even though academic literature already brought the issue of ‘financial instability’ dated back to the writing of Minsky (1982), the global crisis of 2007-2008 make it becomes increasingly serious concerns for policy makers around the globe. The precise definition of ‘financial stability’ differs among academicians and policy makers, but it generally refers to condition in which the financial system functions effectively and efficienty in the economy and resilience in withstanding shocks from both domestic and overseas. Some literature define it in contrast to the conditions that could lead to a financial crisis. Allen and Wood (2006), for instance, referred to ‘financial instability’ as “episodes in which a large number of parties, whether they are households, companies or

governments, experience financial crises which are not warranted by their previous behavior

and where these crises collectively have seriously adverse macroeconomic effects”. ‘Financial stability’ is then described as “a state of affairs in which an episode of financial instability is

unlikely to occur.” More practical definitions can be found in many central banks. For instance, the European Central Bank (ECB) defines ‘financial stability’ as a condition in which the financial


system – intermediaries, markets and market infrastructures – can withstand shocks without major disruption in financial intermediation and in the general supply of financial services.

From the definitions, there are four key aspects that need to be stressed. First, soundness of individual financial institutions is necessary but not sufficient. Financial stability relates to how the system functions for and able to withstand the shocks from macroeconomy. It is more about macro-financial linkages of the financial system than the soundness of individual financial institutions. Second, history tells us that causes of financial crisis are many, but four are the most common: asset (financial and housing) bubbles, credit booms, excessive debt accumulation, and sudden stop of capital flows (Reinhart and Rogoff, 2009; Claessens and Kose, 2013). These macro-financial imbalances tend to be procyclical and propagate the boom-bust of financial cycles in relation to economic cycles, and in most cases precede the financial crises (Claessens, et. al., 2011; Jorda, et. al., 2011). We witness these in Latin America crisis, Asia crisis, US crisis, Europe crisis, and the recent global crisis.

Third, while a financial crisis could emanated from macroeconomy shocks or individual failure of financial institution, its contagion to a systemic crisis evolves through interconnection and networks in the financial markets and infrastructures (Allen, et. al., 2010; Acemoglu, et.al., 2015). Currency crisis, for instance, can be caused by sudden stop of capital flows and then spread out to financial system failures because of foreign exchange market freeze (Calvo and Reinhart, 2000). Similarly, bank runs could lead to bank contagion because of liquidity squeeze in the inter-bank money market (Freixas, et. al., 2000; Morris and Shin, 2004). Fourth, propagation that follows and leads to full-blown and wide-spread financial crisis commonly accelerates through information contagion and herding behavior (Acharya and Yorulmazer, 2003; Bikhchandani and Sharma, 2001). The crisis in the US sub-prime mortgage show how its contagion escalates through fire sales in the financial market and credit squeeze in the banking system (Diamond and Rajan, 2010). Information contagion and herding behavior then lead to wide-spread and multifaceted financial crisis, not only in the US, but also in Europe and around the globe.

Considering the wide range and large negative impacts of a crisis, financial system stability is clearly a shared responsibility. There is no single institution could and should be left alone for assuming this function. There is a need, and now it becomes common practices in many countries, to have a coordination institution or mechanism for overseeing overall financial stability at the national level. Financial supervisory authority, be it in the central bank or a specialized institution, assumes a responsibility for the soundness of individual financial institutions through its microprudential regulation and supervision. The central bank assumes responsibility of the macroprudential regulation and supervision for mitigating macro-financial imbalances and systemic risks of the financial system, in addition to its functions of monetary policy, payments system, and as lender of the last resort. Deposit insurance institution has the role for mitigating the impact of information contagion of bank runs and participate in the


early intervention and resolution of problem banks. And the government, through the ministry of finance, needs to lead the national financial system stability as to prevent the crisis to pose severe detrimental effects to the economy and heavy fiscal burdens.

A country’s central bank is well qualified to assume macroprudential function for regulating and supervising financial stability from the point of view of its surveillance capacity and the policy tools at its disposal (Kawai and Morgan, 2012). Furthermore, the study from 13 developed and emerging countries by Bank for International Settlements, BIS (2011) concluded that the central banks must be involved in the formulation and execution of financial stability policy if such policy to be effective. There are three key reasons why central banks should assume macroprudential policy. First, the performance of their monetary policy functions provides central banks with macroeconomic focus and an understanding of financial markets, institutions and infrastructures needed for the exercise of macroprudential policy. Second, financial instability can be caused by and affect macroeconomic performances, with substantial consequences for economic activity, price stability and monetary policy transmission. And third, central banks are the ultimate source of liquidity for the economy, through its monetary policy and lender of the last resort functions, and appropriate liquidity provision is crucial for financial system stability.

How the central bank incorporates its macroprudential policy for financial stability in its policy making together with its monetary policy and payment systems? This is where central bank policy mix becomes important. For achieving price stability and supporting financial stability, the central banks should not only assess macroeconomic outlooks but also address macro-financial imbalances in the financial system. They are commonly emerge in the procyclicality and build-up systemic risks of asset (financial and housing) bubbles, credit booms, accumulation of external debts, and volatility of capital flows. That said, the following three key concepts constitute the building block of the central bank’s policy mix.

• First,monetarypolicyneedstobedirectedforachievingpricestability,withpaydueregardto asset (financial and housing) prices, be it directly or indirectly. As we know, asset prices bubbles commonly build-up during economic upswing and then bursts that lead to financial crisis and economic recession.

• Second,macroprudential policy constitutes regulation and supervision to financialservices institution from macro perspectives and focuses on systemic risks required for promoting financial system stability. It is geared toward mitigating procyclicality of the financial system (time-dimension), as well as build-up systemic risks that emanate from the interconnectedness and networks of financial institutions, markets and infrastructures, including payment systems (cross-section dimension).

• Third,capitalflowsmanagementisdirectedtomitigateprocyclicalityandbuild-upsystemicrisks from accumulation of external debt and volatility of capital flows. It supports the stability of exchange rate and helps in preventing balance of payments crisis and sudden-stop capital flows that constitute key parts of maintaining financial stability.


Financial market deepening is very important to support the policy mix. As we know, more developed financial market will strengthen the effectiveness of monetary policy transmissions. It also facilitates product innovation and risk diversification in the financial system to support economic financing and financial stability, as well as better absorbs the benefits and reduce the risks of volatile capital flows. Sound prudential measures and appropriate market conducts, nonetheless, need also be strengthened to ensure deepened financial markets would not pose greater risks to monetary and financial stability. As will be discussed in the next sections, greater diversification and product innovation could induce higher systemic risks to financial stability as networks of financial institutions, markets, and infrastructure becomes closer interconnected.

The central bank policy mix is conceptually coherent and practically implementable. The challenge is how to internalize it into integrated policy formulation process in the central bank, supported by, among others, enhanced forecasting model and decision making process. It should be noted that for financial stability, as alluded to above, the central bank’s role and function in macroprudential policy needs to be put as an integral part of overall financial stability policy coordination at the national level. Equally important, the central bank needs to be clear in its communication about to which policy addresses to what objective, based on the policy assignment and exercises in the policy mix.

2.3. Monetary Policy And Financial Stability

How financial stability can be incorporated into the monetary policy framework? There are at least two issues to be addressed, i.e.: (i) incorporation of asset (financial and housing) prices into new dimension of price stability, and (ii) how monetary response to emerging macro-financial imbalances and systemic risks of financial stability. On the first issue, there was debate about whether the Fed long-standing success of low inflation and interest rate was one of the causes of the recent crisis in the US. Taylor (2010), for example, argued that the Fed’s monetary policy stance was too easy, in that it kept the federal funds rate too low for too long, fueling the housing boom and other economic imbalances. Bernanke (2010), on the other hand, disputed this view. The primary cause of housing bubbles was because of exotic types of mortgages and the associated decline of underwriting standards, so that the best response to the housing bubble would have been regulatory, not monetary. Filardo (2001), in other side, found that monetary authority should respond to asset prices as long as asset prices contain reliable information about inflation and output, even if a monetary authority cannot distinguish between fundamental and bubble asset price behavior.

Subsequent empirical findings, however, have fundamentally changed for the supports of central bank policy to response to housing prices in the post-global crisis. Jorda, et. al. (2014), for instance, provide an important evidence on the link between monetary conditions, credit growth, and house prices using data spanning 140 years of economic history across 14 advanced economies. In particular, an exogenous 1 percentage decrease in the short-term


interest rate elevates the ratios of housing price to income and mortgage loans to GDP by about 4% and 3%, respectively, within four years. These historical insights suggest that the potentially destabilizing by-products of easy money must be taken seriously against the benefits of stimulating unsustainable economic activity. Williams (2015), however, warns that there is a very costly tradeoff of using monetary policy to affect housing prices when macroeconomic and financial stability goals are in conflict. Using the same data, he shows that 1 percentage increase in interest rates tends to lower real (inflation-adjusted) housing prices by over 6% within two years, while real GDP per capita declines by nearly 2%. These empirical findings show that central banks need to strike a right balance between price stability and financial stability when formulating its monetary policy

The preceding findings lead to the second issue on how monetary policy respond to macro-financial imbalances and build-up systemic risks reflected in the procyclicality of housing bubbles, credit booms, accumulation of external debts, and capital flows volatility. This issue is closely related to the “lean versus clean” debate: whether it is preferable for the central banks to “leaning against the wind” to manage the bubbles from bursting, or they are better to wait until the bubble bursts and then “clean” up the mess afterward via aggressive monetary policy easing. The “clean” school was adopted by the US Fed under Chairman Greenspan. There are a number of reasons for this view: investment booms were generating by productivity, bubbles may be resulted from declining risk premium and irrational exuberance, and raising interest rate may be ineffective in restraining the bubbles but could cause a bubble to burst severely, thus damaging the economy (Greenspan, 2002).

However, global financial crisis have undermined these arguments. The crisis clearly unveils the potential risks of excessive credit and leverage driven bubbles, and thus provides supports for the “leaning”, rather than cleaning, to prevent such bubbles. The experience in Australia, for instance, shows the leaning could be done and successful. Increasingly concerned about excessive lending in the housing sector in 2002 and 2003, the central bank gradually raised interest rates, even though the outlook for inflation was benign (Bloxham, et. al., 2011). While justify its tightening decision within the framework of inflation targeting, the central bank repeatedly expressed concerns about high credit growth. The consensus has now swung strongly for the central banks in many countries to paying close attention to financial stability and leaning against the wind, even if it is not an official part of their mandate. Bernanke (2009), for instance, stated that the Fed played a major part in arresting the crisis, and it should preserve the institution’s ability to foster financial stability and to promote economic recovery without inflation.

Growing literatures have been devoted to incorporate financial stability into inflation targeting framework of monetary policy. Agenor and da Silva (2013) discuss the integrated inflation targeting regime that incorporates financial stability. In particular, in addition to inflation and output gaps, monetary policy should react to credit gap and real exchange rate to address the time-series dimension of systemic risks. Monetary policy and macroprudential


policy are largely complementary instruments, and thus must be calibrated jointly in the context of macroeconomic models that account for credit market imperfections and effectiveness of monetary transmissions. Woodford (2012) shows the optimal solution for monetary policy when the central bank willing to trade-off a greater degree of stability in price and output-gap for the sake of stabilizing systemic risks of financial stability. Vredin (2015) provides detail descriptions on relevant information for the central bank to incorporate financial stability into the inflation targeting framework. In particular, in addition to macroeconomic, financial conditions and transmission mechanisms, indicators of financial stability relating to financial cycles, financial market vulnerability, and early warning signals would be useful.

2.4. Macroprudential Policy

As stated above, macroprudential policy constitutes regulation and supervision to financial services institutions from macro perspectives and focus on systemic risks required for promoting financial system stability. It is particularly geared toward mitigating procyclicality from macro-financial linkages, as well as build-up systemic risks that could emanate from the interconnectedness and networks of financial institutions, markets and infrastructures, including payment systems. The first objective of macroprudential policies aims to prevent the excessive build-up of risks from the boom and bust of the financial cycles resulting from external factors and market failures (time dimension). The second objective is to make the financial sector more resilient and limit contagion effects from interconnectedness and networks of the financial system (cross-section dimension). These two key objectives constitute the main factors that precede and propagate the instability in the financial system that in many cases lead to a crisis. Another objective of macroprudential, e.g. by the ECB, is to encourage a system-wide perspective in financial regulation to create the right set of incentives and disincentives for market participants (structural dimension). This is important as to manage risk taking behaviour that is a key for both the soundness of individual financial institution as well as for mitigating build-up systemic risks.

A number of studies have documented that the financial cycles tend to be procyclical and amplify the economic cycles (Claessens, et.al, 2011). They tend to precede and propagate build-up systemic risks in the financial system that could lead to a crisis (Claessens and Kose, 2013; Reinhart and Rogoff, 2009)). Empirical studies also reveals four main procyclicality systemic risks that in many cases lead to financial crises: housing bubles, credit booms, excessive accumulation of external debts, and volatile capital flows (Jorda, et.al., 2011, 2014; Calvo and Reinhart, 2000). They are inherent within capitalist economy whereby inflation and debt accumulation have the potential to spin out of control in the period of economic upswing (Minsky, 1982). Stability is destabilizing, and thus leads to ‘boom’ and ‘bust’ in the financial cycles, causing the economy falls into crisis. Macroprudential policy addresses the first two procyclicalities (housing bubles and credit booms) while the other two (external debts and volatility of capital flows) are dealth with capital flows management discussed in the next section.


Interconnectedness and networks in the financial system could also lead to build-up systemic risks during the economic upswing along with procyclicality of the financial cycles discussed above. As explained earlier, during financial distress, contagion and propagation to a systemic crisis evolve through interconnection and networks in the financial markets (e.g. interbank and foreign exchange markets) and infrastructures (including payments system) that lead to liquidity squeeze and market freeze. During the period of economic upswing, financial interconnection and networks could also propagate the upswing of financial cycles into build-up systemic risks. Potfolio diversification beyond certain thresshold increase the risks of financial system failures, even though it may benefits the risk distribution from individual institution. It resembles to complete financial networks that are prone to systemic failures from large shocks or multiple/common shocks (Allen, et. al., 2010; Acemoglu, et.al., 2015). The originate-to-distribute bank model in the case of US sub-prime morgage failure is a clear example.

Propagation of build-up systemick risks during the upswing of financial cycles could also be facilitated through herding behaviour (Bikhchandani and Sharma, 2001). This can be happenning due to a number of reasons. Some banks or investors, especially those with limited information, tend to base their decisions on other reputable banks or investors or advisors rather than their own analysis. Performance measurement system that lead to remuneration and bonus on profit or based on the certain performance benchmark that is commonly practiced in the financial system is another factor behind herding behaviour. In the banking system, Rajan (1994) shows that reputation and remuneration system could lead to fluctuations of lending standard, i.e. tends to ease during upward economic trend and tight during recession. In addition, as in the event of financial distress, information contagion could also lead to herding behavior during the upswing financial cycles (Acharya and Yorulmazer, 2003).

These objectives of macroprudential policy, i.e. macro-perspective and systemick risk focus of financial stability, differs from microprudential regulation and supervision which aim at the soundness of individual financial institutions, banks and non-banks. Individual institution soundness is necessary for financial stability, but not sufficient. Individual failure of financial institutions, if it is not deemed systemic, could be regarded as a problem of capital insolvency due to excessive risk taking, mis-management, and/or loosing competition. This is also as a natural process of banking restructuring and a test for the resiliency of the overall financial system. The case is different to those that are deemed as systemically important banks, which their failures could potentially cause financial instability. BIS (2012) provides a framework for assesing and dealing with domestic systemically important banks based on four main criteria, i.e.: size, interconnectedness, complexity, and substitutiability. They are subjected to stringent rules of regulation and supervision to make them internalize and absorb the systemic risks, including, among others, higher liquidity coverage, total loss absorptive capacity of capital, stricter risk management framework, and requirement for adhering the set-out recovery and resolution plan.


Instruments of macroprudential policy for addressing procyclicality could include loan-to-value ratio for managing credit cycles and counter-cyclical capital buffer, while limits on foreign exchange exposures and offshore borrowing are examples for systemic risks instruments. Microprudential instruments consitutes measures for rating individual bank soundness, managing liquidity risks, minimum capital requirement based on risk profile, and prudent risk management. A number of instruments of both macro and micro regulations may be the same, as they are based on the assesments of liquidity, market, and credit risks. But the objective and perspective of the two regulations differ. The objective of macroprudential regulation is to limit system-wide distress and avoid macroeconomic costs linked to financial instability, while microprudential regulation aims at limiting distress of individual financial institutions to provide protections to consumers (depositors and investors). For some possible common instruments, there are three dimensions that need to be considered, i.e.: individual soundness, systemic risk, and procycliclicality. Thus, for every common instrument could be set the levels that represent requirements from individual soundness, systemic risk, and procyclicality based on assessment of the financial stability during the period. This point of view could be used as an approach for resolving any instrument of regulations that have two objectives of both microprudential and macroprudential.

Recent regulation on capital by the Financial Stability Board (FSB) issued on November 2015 which sets out minimum amounts of Total Loss Absorbency Capacity (TLAC) for Global Systemically Important Banks (G-SIBs) is an example of this approach. The TLAC is to ensure that G-SIBs have the loss absorbing and recapitalization capacity so that, in and immediately following resolution, critical functions can continue without requiring taxpayer support or threatening financial stability. The TLAC is set at 16% to 20% of the capital requirement based on the risk weighted assets and at 6% to 6.75% of the capital requirement based on the total exposure measurement. This applies to G-SIBs that are determined before 2015. First, the Basel III minimum of an 8% total capital ratio based on risk profile must be satisfied for all banks, systemic or not. This level of capital is purely for individual bank soundness. Second, the various Basel III buffer requirements must also be met, i.e.: capital conservation buffer for systemic risks, counter-cyclical capital buffer for addressing procyclicality, and capital surcharge for G-SIBs. The level of counter-cyclical buffer could be set higher or lower according to the extent of procyclicality at certain period. And third, additional regulatory capital and debt instruments with a minimum remaining maturity of one year that are subordinated to all other creditor claims in insolvency (eligible debt instruments) can then be included in the TLAC. Such approach of structuring regulation according to the objectives of individual soundness, systemic risk, and procycliclicality could also be applied to other instruments that are both microprudential and macroprudential in natures.


2.5. Capital Flows Management

Capital flows management (CFM) aims to mitigate procyclicality and build-up systemic risks from accumulation of external debt and volatility of capital flows. It supports the stability of exchange rate as well as helps in preventing balance of payments and sudden-stop crises that constitute integral part of financial stability. That capital flows have many benefits to the economy are widely aknowledged (Koepke, 2015). FDI and long-term banking flows could facilitate domestic investments and, if they are accompanied by productivity in the economy, increase growth. Nonetheless, some banking and portfolio flows are volatile, particularly those of short-term and speculative natures, and could pose risks to macroeconomic and financial system stability. These flows could surge in some period and reverse in other period, responding to relative magnitude between “push factors” of global output, interest rate and risk aversion in one side, and “pull factors” of domestic output, asset returns, and country risk in the other side. The surge of capital inflows to the EMEs during the period since the global crisis and their reversals following the Fed tapper tantrum in mid-2013 provide a clear example. Increasing volatility of capital flows poses central banks in the EMEs serious challenges in safeguarding monetary and financial stability.

The best defence for the EMEs to better absorb capital flows and reap their benefits is by implementing sound macroeconomic policies, exchange rate flexibility, deepening financial markets, strengthening financial regulation and supervision, and improving institutional capacity (IMF, 2012, 2013, 2015). But surges of inflows can lead to macroeconomic and financial instability, signs of economic overheating or asset bubles, strong currency appreciation, rapid credit expansion, and build-ups systemic risks in balance sheet and other vulnerabilities that can induce sudden-stops or reversals of these inflows. Under such circumstances, interest rate increase will not be effective as it will induce more capital inflows, especially when inflation is under controlled. Foreign exchange intervention could moderate exchange rate appreciation and at the same time increase the international reserves for building up buffers in case of capital reversals. Increasing reserve requirements could absorb excess liquidity in the domestic banking system from capital inflows. To support these policies, the CFM could be implemented in the forms of tax on portfolio equity and debt inflows (Brazil, 2009), holding period on central bank bills and limit on short-term foreign borrowing by banks (Indonesia, 2011), withholding tax on interest income on nonresident purchases of treasury and monetary stabilization bonds (Korea, 2011), fee on nonresident purchases of central bank paper (Peru, 2010), or withholding tax on nonresidents’ interest earnings and capital gains on new purchases of state bonds (Thailand, 2010).

Similarly, large, sustained, or sudden outflows can give rise to macroeconomic and financial stability risks. In this regards, increasing interest rate can be used, especially when there is pressure to inflation. Foreign exchange intervention could moderate exchange rate depreciation, as long as it does not cause serious depletion on the adequacy of international


reserves. Reducing reserve requirement is also an option, and relaxation of the existing CFM could be implemented. Additional CFM measures could include imposition of 12-month waiting period for nonresidents to convert proceeds from the sale of securities (Malaysia, 1998), limits on forward transactions and introduction of export surrender requirements (Thailand, 1997), limit bank withdrawals and imposed restrictions on transfers and loans in foreign currency (Argentina, 2001), stop of convertibility of domestic currency accounts for capital transactions (Iceland, 2008), and a 5-day waiting period for nonresidents to convert local currency proceeds from investment transaction to foreign currency (Ukraine, 2008).

III. METHODOLOGY

To assess the policy mix setting and impact, Bank Indonesia has constructed some empirical models. On this paper, we recall the result of these models and confront it with real data dynamics. The complexity of the model varies according to the aim of the policy analyses, the scope of the policy coordination, the number and the types of internalized variables, and the time horizon of the analysis. Most of these models use the inflation targeting as main framework and then carefully incorporates stability and other aspect of concerns.

In addition to enlarging macroeconomic forecast and analysis to include macro-financial linkages for the formulation of macroprudential policy, we developed models to assess the optimal lending growth of the banks (see Utari et.al, 2012). We apply the model to aggregate lending growth as well as lending growth to each bank, certain types of lending (consumption, working capital, and investment), and per economic sectors. By comparing these optimal vs. actual lending growth, we can determine where excessive lending occurs and assess their build-up systemic risks. Analyses of procyclicality of bank lending are useful in determining the timing of the counter-cyclical measures. And we assess what and when some instruments of macroprudential measures are justified and can be applied.

Within the central bank, we enlarged the existing macroeconomic policy forecasting and analysis model to include external and banking sectors to better capture macroeconomic and macro-financial linkages with corresponding interest rate, exchange rate, reserve requirement, and LTV ratio.

IV. RESULT AND ANALYSIS

Indonesia is an inflation targeting country, introduced in 2003 and implemented strictly since 2005. The framework suits well in bringing down inflation from about 9% in 2003 to now within the target range of 4±1%. With subsidy has been revamped at the end of 2014—the main factor behind high administered prices shocks in the past, inflation will be more under controlled and continued on the declining trend toward a medium-target of 3±1%. From the institutional


aspect, the framework has been successful for the central bank in gaining monetary policy credibility. The discipline that it brings to the formal and regular policy forecasting and decision making process in the monthly board meetings put the central bank in forefront in keep updating the macroeconomic outlook and policy responses needed for achieving price stability. These regular assessments also serve well for close policy coordination between the central bank and government in formulating fiscal, monetary and structural reforms to safeguard macroeconomic stability and supporting economic growth. Moreover, the aggressive communication by the central bank provides important instrument to anchor inflation expectation as well as broader macroeconomic outlooks. Overall, the already established framework provides strong foundation for the central bank to assume macroprudental policy for promoting financial system stability.

The challenges for the success of implementing central bank policy mix in Indonesia come from both domestic and external. First, the Indonesian economy is widespread through archipelagoes and dependence on commodity, and thus subjected to recurrent shocks from foods prices inflation and current account imbalances. Addressing these internal and external imbalances through monetary policy is a key for the success of maintaining both macroeconomic and financial stability. Second, the financial system is bank dependenced with shallow financial market. Managing procyclicality and systemic risks of the banking system through macroprudential regulation and supervision not only will be a key for financial stability but also strengthen the effectiveness of monetary transmissions. And third, Indonesia is small economy with fully open capital account, and thus management of volatile capital flows is very important. These three challenges of monetary policy, macroprudential policy, and the CFM needs to be addressed in the central bank policy mix.

Furthermore, these challenges are closely linked and intertwined, making the policy mix even become utmost requirement. During economic upswing, e.g. because of commodity prices boom, financial deregulation, or favorable global environment, accelerated domestic demand then created credit boom, property bubbles, high inflation, widening current account deficit, and accumulation of external debt. We witness this in the history of big and mini crises in Indonesia. The economic bonanza following the broad based financial deregulation during 1983-1988 ended up in deep crisis in 1997-1998 which unveils those serious macro-financial systemic risks. The mini crisis in 2005 was led by rapid growth of domestic demand, bank lending, and large capital inflows following global commodity boom. We also record recurrent problems during 2010-2013 whereby commodity export induced domestic demand acceleration created large current account deficit when commodity cycle was sharply reversed. These macro-financial imbalances could not be resolved either by monetary policy, CFM, or even by microprudential regulation and supervision. Macroprudential policy is the additional instrument, and it needs to be integrated with monetary policy and the CFM of the central bank.


4.1. The Policy Mix

As discussed above, two issues are of particular important to incorporate financial stability issues in monetary policy under (flexible) inflation targeting, i.e.: (i) enlarging the scope of price stability to include assessment of asset (financial and housing) prices, and (ii) addressing procyclicality and build-up systemic risks in the macro-financial linkages. For the first issue, in addition to CPI inflation, we put particularly emphasis on the assessment of exchange rate, government bond yield and equity prices, and housing prices. For the exchange rate, we already incorporate it into our macroeconomic forecasting and policy analysis model in setting monetary policy response. Consistent with the inflation targeting framework, the ultimate objective remains the CPI inflation. The inclusion of exchange rate in the model provides useful exercises on the consistency of (market-based) exchange rate and guidance on exchange rate policy to deal with possible excessive misalignment that is risking both the achievement of the inflation target and in support for financial stability. On the other asset prices, we opt to analyze them separately (outside the model), but they enrich our understanding on the overall macroeconomic forecasting and what monetary and/or macroprudential instruments that are best suited to address emerging risks.

On the second issue, to enrich our better understanding of macro-financial linkages, we enlarge our macroeconomic forecasting model to include external default risk as a proxy for sudden-stops and credit gaps to measure procyclicality in the banking system (Harmanta, et.al, 2012, 2013). The model provides policy scenarios with the interest rate response (Taylor rule type) and reserve requirement from monetary policy and/or loan-to-value ratio as possible macroprudential instruments. Since the forecasting model is forward looking, it sheds important considerations on how best to lean against the possible risks from sudden stops and build-up systemic risk of financial stability, i.e.: through monetary policy interest rate or macroprudential measures or combination of the two. To sharpen our understanding on the procyclicality and macro-financial cycles, particularly credit booms and housing bubbles, we run separate models for assessing the nature of their cycles and possible build-up systemic risks that are foreseen over the policy horizon, at the aggregate level and cross-section (Alamsyah et.al, 2014; Harun et.al, 2014).

From the financial stability perspectives, we run in-depth assessments that are suggested in the literature (Bisias, 2012) and practiced in a number of central banks (e.g. EBA, 2015). We focus on systemic risks assessment (not individual soundness rating) of systemically important banks, both from top-down and bottom-up approach, of their key risks of capital, asset quality, liquidity risk, market risk, and earnings. Assessments on the inter-connectedness of those banks in the interbank market and payment system are also conducted. Tail-risk analyses on the credit risk are performed by several methods such as probability of default and transition matrix of asset quality. Stress-test of financial stability to the banking system on its resilience to macroeconomic shocks through integrated and/or balanced approaches based on the risk


survey that we introduces. Risk assessments are also conducted to corporate and household balance sheets on their financial performances on how theses would impact to the banking system risks. To facilitate theses assessment of macro-financial linkages among the financial system, both from the procyclicality and build-up systemic risks, we are developing statistics on balance sheets interlinkages among economic agents and financial system, both public and private, at the national level and cross-geographical within sub-national levels.

Based on the overall assessment of monetary policy forecasting and analysis as well as of time-dimension and cross-section of financial systemic risk assessments, the central bank policy mix consists of the following four main instruments (Warjiyo, 2014a, 2015b). First, as in the inflation targeting framework, interest rate is set to ensure that inflation forecast to fall within the targeting range, i.e. at 4±1% in 2016 and 2017. Second, exchange rate policy is geared toward maintaining the stability of exchange rate movements along its fundamental trend to ensure their consistency with the achievement of inflation target and to avert their excessive volatility that may put pressures on the financial stability. Third, capital flows management is conducted to support the exchange rate policy, particularly in the period of large surge of capital inflows and heightened risks of capital reversals, for achieving monetary and financial stability. Fourth, macroprudential policy is geared towards maintaining financial stability and supporting the effectiveness of monetary policy transmission. Financial market deepening is also accelerated to support the effectiveness of the policy mix. The central bank is also engaging close coordination with the government, both at the central and sub-nationals, for macroeconomic management, as well as with financial services authority and deposit insurance institutions on matters relating to the national financial system stability. Clear communication is very important for the success of the policy mix.

A key question is how to mix the monetary and macroprudential policies in responding to different cases that may give rise to conflict between price stability and financial stability objectives. This is an open debate as it deviates from the Tinbergen rule of one instrument for one policy objective. But there is convergence view that there are many cases that both instruments are complimentary for achieving both objectives (Yellen, 2014). The following table presents four cases of price stability and financial stability risks based on forward looking macroeconomic and macro-financial forecast and analysis over the policy horizon, and their corresponding mix of monetary and macroprudential policy stances. At the first quadrant, where forecasted risks to both price and financial stability are low, it is natural that both monetary and macroprudential policy stances are neutral. At the other extreme of fourth quadrant, where forecasted risks to both price and financial stability are high, it is natural that both monetary and macroprudential policy stances are tight.


Table 1. Four Cases of Price and Financial Stability

FORECASTED RISK OF PRICE STABILITY

LOW HIGH

FORECASTED RISK OF FINANCIAL

STABILITY

HIGHQuadrant II

• MonetaryNEUTRAL/LEANING• MacroprudentialTIGHT

Quadrant IV• MonetaryTIGHT• MacroprudentialTIGHT

LOWQuadrant I

• MonetaryNEUTRAL/EASING• MacroprudentialNEUTRAL/EASING

Quadrant III• MonetaryTIGHT• MacroprudentialNEUTRAL/LEANING

The potential conflicts are in the second and third quadrants. In the second quadrant, where forecasted risks of price stability is low but of financial stability is high, the stance of macroprudential policy is clearly tight. In this case, monetary policy could help macroprudential policy in leaning against the forecasted risks of financial stability in the policy horizon. This is the case in the US in the period preceding the global crisis as debated between Taylor (2010) and Bernanke (2010) as discussed above. In the third quadrant, where forecasted risks of price stability is high but of financial stability is low, the stance of monetary policy is clearly tight. In this case, macroprudential policy could help monetary policy in leaning against the forecasted risks of price stability in the policy horizon. The extent to which and choice of macroprudential measures will depend on the factors that give rise to forecasted risks of price stability. A natural selection could be directed toward reinforcing the channels of monetary transmissions in safeguarding price stability. For instance, where risks to price stability stems from strong domestic demand induced by bank lending to housing sector, a loan to value ratio targeted to these sector is an option to be considered.

The factual problems in the real world may not be as simple as just described, of course. But we think this approach could be used as useful guiding principles to address the possible conflicts that may arise between price and financial stability objectives. Again, the extent to which and choice of monetary and macroprudential measures will naturally depend on the corresponding factors that give rise to forecasted risks of price and financial stability in the respective countries. We also think the same approach could be used to address the policy trilemma of monetary independence in achieving price stability, exchange rate stability, and capital mobility as we know in the international finance (Obstfeld, 2015). The following sub-sections will discussed how we implement this approach and the choice of instruments in the central bank policy mix in managing monetary and financial stability in Indonesia during the periods of heighten global economic and financial turbulences since the global crisis.


4.2. Interest Rate And Exchange Rate Policies

Under the inflation targeting framework, our decision on interest rate is to ensure the achievement of inflation target. The issue is how to deal with exchange rate movements that may give rise to the risks of forecasted inflation off the target. This is particularly the case for many EMEs when facing unprecedented large volatility of capital flows since the global crisis. In contrast to the advanced countries, exchange rate stability matters for the EMEs due to a number of reasons, e.g. under-developed domestic financial market, their detrimental effects to banking conditions and financial stability, and rigidity in the economy. Under such circumstances, dual targeting of exchange rate for achieving the inflation target will strengthen the monetary policy credibility under the inflation targeting framework (Ostry et. al., 2012). Specifically, exchange rate targeting could be used to mitigate the unintended impacts of capital flows to the inflation target, both directly via exchange rate pass-through and indirectly through domestic demand. Many EMEs have included exchange rate in the Taylor rule (Mohanty and Klau, 2004; Aizenmann et. al., 2011). Foreign exchange intervention is another option. When capital flows causing significant exchange rate misaligned from its fundamental and inflation will be off the target, a combination of interest rate responses and foreign exchange intervention would be more effective and thus strengthen monetary policy credibility.

We adopt this approach since 2010 and find it superior than the standard inflation targeting relying solely on interest rate. Three episodes since the global crisis provide evidences, i.e. the period of 2010 to the Fed tantrum in May 2013, the period since the Fed tapper tantrum to mid-2015, and the period since then. During the first period, Indonesia enjoys most of the favorable spillovers from the global crisis, particularly high commodity prices and surge in capital inflows (Warjiyo, 2013b). Economic growth was high at the peak of 6.5% in 2011 and moderate slightly at 6.3% in 2012. Inflation was at the lowest history of 3.8% in 2011, even below the lower bound of the target of 5±1% at that time, and only slightly increased to 4.3% in 2012. During this period, Indonesia received large capital inflows, driven by both global excess liquidity searching for higher yield and Indonesia promising economic outlook. Exchange rate appreciated by the surge in capital inflows, corroborated with favorable current account surplus from the high global commodity prices. The challenge is how to manage these inflows to mitigate build-up systemic to financial stability as bank lending growth was high at above 23% per year during 2010-2012. This is the case of second quadrant where the risks of price stability are low while of financial stability are high as discussed above.

Consistent with the inflation targeting framework, the central bank cut the policy rate by 75 bps from 6.5% in 2010 to 5.75% in 2012. Further cuts of policy rate would not be consistent with the inflation targeting framework as inflation at the historically low. It would not be effective in stemming the capital inflows driven more by ‘push factors’ than ‘pull factors’ (Indrawan et.al, 2013). And it was also not consistent with the financial stability objective as bank lending growth was excessively high. The central bank intervene in the foreign exchange market


to stem the surge in capital inflows as well as to moderate the exchange rate appreciation. To sterilize its impact to domestic liquidity more effectively, reserve requirement was raised from 5% to 8% in November 2011. The international reserves increased significantly from a mere of US$ 66.2 billion at the beginning of 2010 to the peak of US$112.8 billion in 2012. It turned out that the increase of reserves provided important buffers for the capital reversals following the Fed tapper tantrum in mid of 2013.

The situation was then reversed. Large capital reversals immediately followed the surprise Fed tapper announcement, running over the months of May to August of 2013. The sudden reversals from both government bonds and equity markets in such a short period created herding behavior that was put both monetary and financial stability at risks (Warjiyo, 2014b). The problem was aggravated by widening current account deficit at the peak 4.4% of GDP as exports fall due to the plunge of global commodity prices while imports continued to increase at the back of strong domestic demand. Inflation surge to 8.4% in 2013 as the government raised the fuel price in July 2013 and to 8.3% in 2014 as fuel subsidy was removed in October 2014. From financial stability, bank lending growth was still high at 21.4% in 2013. This is the case of fourth quadrant as risks to both price and financial stability were high.

The central bank responded swiftly to stabilize the situation: raising interest rate and tightening macroprudential. Indonesia is among the first central bank that ahead of the curve raised its policy rate in the aftermath of Fed tapper tantrum. We started to increase the policy rate by 25 bps in June 2013, and then aggressively raised it consecutively in the following months with a total of 175 bps to 7.50% within six months to November 2013. The primary objective was to preemptively contain the inflation pressures stemming from fuel price hike. The aggressive moves also to slow down the domestic demand to reign in current account deficit. Timing of the decisions were perfectly match the needs to respond to the capital reversals. We believe bold and aggressive response in interest rate is a key to send a strong and clear signal to the market for monetary policy credibility.

The central bank also intervened heavily in the right aftermath of the Fed tapper tantrum to stabilize the exchange rate before it resumed since September 2013. The intervention caused the reserves to fall to the lowest of US$ 92 billion in September 2013 before it recover to US$99 billion at the end of 2013. The intervention is supported by central bank purchases of the government bonds in the secondary market, especially during the period of heavy capital reversals, a tactic that we call dual intervention (Warjiyo, 2013c). This is in essence to make sterilization more effective, as purchasing bonds from secondary market release the liquidity squeeze because of capital reversals that could not be compensated by foreign exchange intervention. It also strengthens the effectiveness of intervention in stabilizing the exchange rate. The central bank send clear signals to stand ready to supply the foreign exchange and at the same time buy the bonds that foreign investors wish to unwind, and thus avoiding herding behavior and contagion of escalating capital reversals. Moreover, the dual intervention is a way


to bring about the objective of monetary stability to be consistent with maintaining financial system stability. By stabilizing the foreign exchange market and government bond market, the dual intervention helps in stabilizing the overall financial markets.

The bold monetary policy adjustments pay off and gain credibility. Market confident quickly resumed and capital inflows were flourish since end of 2013 and throughout 2014. Macroeconomic and financial stability remain intact. In fact, inflation was down from 8.3% following subsidy reform in 2014 to 3.3% in 2015 and current account deficit quickly narrowed from 3.3% to 2.0% of GDP during the same period. This is the case of first quadrant, whereby risks of both price and financial stability is low. Nonetheless, economic growth slowed down from 5.2% in 2014 to 4.9% in 2015, and bank lending growth is tight at about 10%. With stability intact and the Fed policy communication becomes clear of gradual normalization process, the central bank cuts the policy rate three times a total of 75 bps during the first three months of 2016 to 6.75% at present. Reserve requirement was also lowered by 50 bps in November 2015 and again by 100 bps to 6.5% in February 2016. We believe the monetary easing will reinforce fiscal stimulus to support economic growth with the inflation is forecasted to be within the target range of 4±1%. Together with accelerated structural reforms, Indonesia economic growth will be around 5.2-5.6% in 2016 and increase to 5.3-5.7% in 2017.

4.3. Capital Flows Management

The CFM in Indonesia is to complement, not substitute, sound macroeconomic policy. We continue to believe that the best defense for mitigating the global spillovers is strong economic fundamentals, sound macroeconomic and financial system stability, and accelerated structural reforms. Specifically, the CFM Indonesia is guided with the following three principles. First, the objective is to mitigate the negative impacts of short-term volatility in capital flows to instability of exchange rate as well as the overall monetary and financial system. Second, they are targeted, i.e. to short-term and speculative capital flows, as we welcome those inflows that are of medium-longer term that benefits the economy. Third, the measures are consistent with our broad principle of maintaining free foreign exchange system. As much as possible, we do not differentiate resident with non-residents. And they are temporary, i.e. the measures are strengthened when too much capital inflows and relaxed when too much capital outflows.

Followings provide clear examples. During the period of heavy capital inflows to the Fed tapper tantrum, we introduced in 2010 measures of CFM in the forms of six month holding period for transactions in the central bank bills and imposed a maximum of 30% capital to the short-term off-shore borrowings of the banks. But in the period following the Fed tapper tantrum in 2013 we relaxed the holding period to one month and expanded a number of transactions that are excluded from the calculation on off-shore borrowing of the banks. We view that these measures help in dampening the short-term and volatile capital flows, and thus are consistent with the objective of managing price and financial system stability. Significant


progress in financial market deepening provides better facilitation to these capital flows and greater exchange rate flexibility. In the foreign exchange market, for instance, the introduction of JISDOR (Jakarta Interbank Spot Dollar Rate) as market reference for exchange rate has been a success and there has been significant increase in the transactions of hedging instruments (e.g. swaps and forwards) in the market. Repo market is also progressing in the money market.

We also introduced a new regulation at the end of 2014 for strengthening risk management of non-bank corporate external debts. In Indonesia, public debts are under controlled by the law limiting fiscal deficit of both central and sub-national government to maximum 3% of GDP. For banks, in addition to limit on short-term borrowing above, they are required to seek the central bank approvals to ensure their external borrowing consistent with the objective of macroeconomic and financial system stability. Under the new rule of 2014, non-bank corporate external debts are subjected to strengthened risk management in the forms of requiring them to have: (i) currency hedging ratio of minimum 25% of their net external debts due within three and six months, (ii) liquidity ratio (including the current foreign assets in the hedging ratio) of minimum 50% of their net external debts due within three and six months, and (iii) a minimum credit rating of one notch below the investment grade. The effectiveness is encouraging, as about 88% of more than 2000 companies that submit their quarterly financial reports in 2015 to the central bank comply with the regulation. The new rule have also positive impacts to domestic foreign exchange market deepening as hedging instruments in the forms of swap and forward increase significantly.

4.4. Macroprudential Policy

Following the empirical model explained in methodology, we assess the macroprudential policy particularly the optimal lending growth of the banks to determine if the bank’s lending is excessive and build up systemic risk. This is the approach that we applied when introducing loan-to-value (LTV) ratio to lending to automotive and property sectors averaging at about 70 percent in 2012 (Warjiyo, 2015a). As discussed above, while price stability remains under controlled, we faced build-up risks to financial stability as bank lending growth was rapid during this period. To strengthen the adjustment needed to ensure macroeconomic and financial stability following the Fed tapper tantrum, we then strengthen the LTV ratio to lending to property sectors in 2013, especially to mortgages for the second, third, and so on purchases of certain types of housing and apartments. The measures are also complemented by supervisory actions to banks that we viewed exhibit excessive lending behavior. We note that the formulation and implementation of macroprudential measures require a much detail and complex analysis and calibration, as well as the need for clear communication to the banks and business community.

Our experience shows that the macroprudential measures and supervisory actions help in reinforcing the effectiveness of monetary transmission mechanism and supporting the financial system stability (Purnawan and Nasir, 2015; Wimanda et.al, 2012, 2014). Even though lending


growths increased in the period prior the implementation of these measures, probably because banks and their customers wanted to utilize the interim period, they declined substantially in relatively short-period in the subsequent episode. The growth of mortgage on housing for less than 21 square meter, for instance, declined from more than 100% to the negative growth during the period of June to September 2012. Likewise, the growth of mortgage on apartment less than 21 square meter dropped from more than 300% to less than 10% during the period of January to November 2013. It should be noted that the automotive and property sectors contain substantially large import content, and thus managing lending growths to these two sectors help in reducing the current account deficit.

Subsequently, we relaxed our macroprudential measures by increasing the LTV ratio by an average of 10% in June 2015. As discussed above, our forecasted risks to both price and financial stability based on macroeconomic and macro-financial forecasts and policy analysis were low, a case of first quadrant. Nonetheless, the use of interest rate policy was constrained during that time due to uncertainty of the Fed fund rate increase. That is the reason why we started our easing policy stance with relaxation of macroprudential measures in June 2015, and then followed by policy rate cuts started in January 2016. We believe our recent policy mix of policy rate cuts, lowering reserve requirement, and relaxing macroprudential, together with accelerated fiscal stimulus and structural reforms by the government, will reinforced each other to deliver better economic prospects of Indonesia with higher economic growth and sound macroeconomic and financial stability this year and beyond.

As a part of its macroprudential policy, the central bank started to adopt a regulation on Countercyclical Capital Buffer (CCB) since end of 2015. Consistent with the easing stance of the central bank policy mix, the CCB is currently set at 0% and will be reviewed every six month. The central bank’s adoption of CCB is in accordance with the international standard on macroprudential policy.

4.5. Institutional Setting

The effectiveness of central bank policy mix needs to be supported by strengthening institutional setting within the central bank and its close coordination with the government and related agencies. As we explained on methodology, we did enlarged the model to also include the external and banking sectors. Researches are conducted to better understand the behavior of capital flows and procyclicality of bank lending. More researches are underway to have more insights on the macro-financial linkages, procyclicality, and systemic risks. Better data and statistics are also important, including development of financial stability indicators and statistics on balance sheets interlinkages in the national as well as sub-national levels.

On the decision making process, there is debate to which better option to continue separate committees or to have joint committee for monetary policy and financial stability. Kohn (2015),


for instance, prefers to have separate committee, considering differences in objective and focus, instruments, as well as for accountability. He cited the experience in the Bank of England setting up three committees outside monetary policy committee after the global crisis, i.e.: Prudential Regulation Authority (PRA) was set up under the Bank to conduct microprudential, Financial Conduct Authority (FCA) to oversees the financial markets, and Financial Policy Committee (FPC) for macroprudential policy. In Indonesia, we do not have the problem since the board of central bank is one board that oversees all of monetary, macroprudential, and payment system policies. The central bank do have separate committees chaired by deputy governor of each monetary, macroprudential, and payment system policies. To support the central bank policy mix, a joint policy committee is set up before the board meeting to integrate the analysis of macroeconomy and financial stability, as well as to coordinate recommendation on the policy mix. We find the joint policy committee enrich our understanding of the interlinkages between macroeconomy and financial system, and what policy mix that better suits for achieving price stability and supporting financial stability.

The central bank is also in close coordination with the government and other related agencies. Coordination of monetary and fiscal policy is closely conducted between central bank and ministry of finance in the budget formulation as well as other aspects of macroeconomic management. Even though the central bank is independence, its policy mix constitutes an integral part of macroeconomic policy mix of monetary, fiscal, and structural reform at the national level (Warjiyo, 2013a). On financial system stability, coordination is done through Financial Stability Policy Coordination Committee (FSPCC) chaired by Ministry of Finance with members of Bank Indonesia, Financial Service Authority (IFSA), and Deposit Insurance Institution (IDIC). A new law on prevention and resolution of financial system stability was just passed which provides strong legal foundation of roles and responsibility of each institution on financial stability, dealing with systemically important banks, and crisis prevention and resolution mechanism. The central bank’s macroprudential policy is also closely coordinated with the FSA’s microprudential regulation and supervision.

V. CONCLUSION

We already present the key concepts and implementation of central bank policy mix in meeting the renewed mandate for achieving price stability and supporting financial system stability. It comprises four key elements of policies on interest rate, exchange rate, capital flows management, and macroprudential. The renewed mandates of central bank on price and financial stability are complimentary. We present four different cases of price and financial stability that warrant different policy mix. Monetary policy with inflation targeting framework serves as a foundation for the policy mix. The key is to enlarge the standard macroecnomic policy forecasting and analysis to incorporate macro-financial linkages to assess procyclicality


of the financial system and build-up systemic risks, and the corresponding policy mix that is consistent with the emerging problems.

Our experience with the central bank policy mix in Indonesia since 2010 shows that it is superior than the standard inflation targeting framework. We present three episodes with the policy mix that play important role for Indonesia resilience in withstanding the global spillovers. To support the policy mix, we enlarge our policy forecasting and analysis model to encompass macro-financial linkages, especially external and banking sectors. A number of researches are developed to better understand the macro-financial linkages, focusing on the procyclicality and systemic risks from capital flows, private external debts, housing bubbles, and bank lending. Internal decision making process has also been strengthened by introducing joint monetary and financial system stability committee within the central bank to formulate the policy mix.

Closer coordination with the government and related agencies has been strengthened. At the national level, the central bank’s policy mix constitutes an integral part of economic policy mix of macroeconomic policy, financial system stability policy, and structural reforms. Accelerated structural reforms aim at achieving higher output potential for economic growth. Coordination on fiscal and monetary policy is geared toward managing economic cycles for maintaining both macroeconomic internal balance (low inflation) and external balance (sustainable current account). At the same time, policy coordination on financial system stability, including macroprudential policy of the central bank, aims at managing financial cycles and mitigating systemic risks for promoting macro-financial balances. These measures of national economic policy mix is very important for achieving sustainable economic growth with sound macroeconomic and financial stability.


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387Impact of Financial Inclusion on Financial System Stability in Asia

Impact of fInancIal InclusIon onfInancIal system stabIlIty In asIa

Azka Azifah Dienillah1

Lukytawati Anggraeni2

Financial inclusion is one of strategy to increase inclusive growth in Asian countries. However, it

may cause either stability or instability in the financial system. Therefore, this research aimed to analyze

the relationship between financial inclusion and financial stability and to analyze factors that affect the

stability of the financial system in seven Asian countries in the period of 2007-2011. The methods used

are Pearson correlation and Fixed Effect Model. The results show that there is negative correlation at

5% significant level between financial inclusion and financial stability. Factors that significantly affect the

financial stability are financial inclusion, financial stability in the previous period, non-FDI capital flows

to GDP, the ratio of current assets to deposits and Short-term funding, and GDP per capita. Thus the

increase in financial inclusion, current assets of banking, GDP per capita, and the portfolio investment can

become the strategies to improve the financial stability (Bank z score) on the determined and future year.

abstract

Keywords: Pearson Correlation, Regression Model, Financial Inclusion, Financial Stability, Banking Sector

JEL Classification: C1, C5, E6, G1, G21

1 Azka Azifah Dienillah is a Economic Postgraduate Student of Bogor Agricultural University; email: [email protected] 2 Lukytawati Anggraeni is a lecturer in Department of Economic, Bogor Agricultural University


I. INTRODUCTION

Inclusive growth or comprehensive growth is one of the important objectives of economic policies in the world, especially in Asia. Government, development partners, and economists have given a lot of attention to inclusive growth in economy and social fields, especially in access to education, health care, social security, clean water and sanitation, transportation and electricity, as well as financial services to all segments of the society (ADBI 2014). Inclusive growth in Asia continues to be promoted due to the increasing inequality of average income from year to year.

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Table 1 shows that the Gini index of Asia in the period of 1980-2010 showed an increasing trend. In 1990 the Gini index of Asia had a value of 0.27 but by the end of 2013 it reached the level of 0.46. In other words, the Asian Gini index increased by 25.3% from 1990 to 2013. This value is relatively high when compared to the increase in world’s Gini index at 4.2% from 1990 to 2013. This increase in the Gini index of Asia, resulted in the implementation of strategies to improve inclusive growth: one of them through the promotion of financial inclusion. Financial inclusion is a set of strategy to improve people’s access to financial services by eliminating all forms of barriers, both price and non-price barriers (Bank Indonesia, 2014).

The financial inclusion program in Asia was started by intensive improvement in better access to communities who have not profited the services of formal financial institutions due to some access barriers. The barriers are caused by the lack of public knowledge on the functions of these financial institutions and the products offered by these financial institutions which do not cater the needs of low-income community members. In addition, according to Kunt et al. (2008), these access barriers to banking services may be due to the bank’s own business model, market position, the on-going level of competition, macroeconomic conditions, as well as the implemented agreements and regulations. Despite these obstacles, the average growth of financial inclusion in some Asian countries shows a stable increase.


Figure 1. Financial inclusion index insome Asian countries in period 2005-2013

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Figure 1 shows that the average financial inclusion in some Asian countries is in rising trend with an average rate of increase at 3.7% from the period 2005 to 2013. South Korea has the highest index of financial inclusion with an average rate of 2.6%, since the people of South Korea have a high level of access and utilization of financial services with little access barriers. While Indonesia has an increase rate of 3.56% in its financial inclusion index and is the lowest among of the seven countries due to low level of access and use of financial services with a great number of access barrier. According to the research of Camara and Tuesta (2014), South Korea is ranked number 1 in terms of people’s access to financial services, while Indonesia is ranked 61 out of 82 countries in the research. In the presence of access barriers to the financial services, South Korea ranks at 14 and Indonesia at 71. This result is supported by data from the World Bank (2011), in which South Korea has a high access to the formal financial institution account ownership percentage for people 15 years old at 93%, compared to Indonesia at 20%, meanwhile the percentage of loan in South Korea is at the level of 17% and 9 % for Indonesia.

In order to increase financial inclusion in these Asian countries, some excellent programs have been developed to increase the community access to financial services and to reduce the barriers. For example, Indonesia has several specific strategy to improve financial inclusion such as Kredit Usaha Rakyat (KUR), d-KU program, E-Money, Telkomsel Cash, “Ke Bank” Program, “to Bank” and microfinance services improvement program. Another example is Thailand, which has two specialized institutions with a mission to improve financial inclusion: Village Fund and the Bank for Agriculture and Agricultural Cooperatives (BACC), and India which has deployed several programs such as Agent Banking, Mobile Banking, and other unique innovations such as Biometrically Scanned which is a financial identity system for the Indian population to facilitate the provision of financial services as a whole (ADBI 2014).


The increase of inclusive growth is the main goal of financial inclusion programs which have been carried out by Asian countries through the reduction of poverty, overall development improvement, equitable distribution of income, and the improvement of the financial system stability. The financial system stability is expected to be as shock absorbers for the Asian countries if another global financial crisis as the one in 2008-2009 reoccurs.

Behind the vigorous implementation of the financial inclusion programs as well as the research findings on positive and negative impacts of the financial inclusion on development and poverty reduction, financial inclusion may also bring positive and negative impact to the financial system stability. The positive impact comes from the improvement in banking assets diversification, stability of the deposit base, and monetary policy transmission. Meanwhile the negative effects are resulted from: (1) the reduction in credit standards since financial institutions are seeking to reach out the unbankable, lower economy clients by lowering the terms of credit; (2) the increased risk of the bank’s reputation because some countries may lower the financial establishment standards to increase the financial service facilities in rural areas, and (3) the instability due undeveloped and insufficient regulations for microfinance institutions (Khan, 2011).

Financial inclusion programs are intended to promote inclusive growth by poverty reduction, economy development, equitable distribution of income and the improvement of financial system stability. Some studies have suggested that financial inclusion can promote better development and reduce poverty in many countries. Research on financial inclusion impacts on the overall development has been widely conducted, such as Sarma and Pais (2011) who used the OLS method and concluded that the level of human development and financial inclusion has a positive relationship for several countries in the world. In addition, Levine (1997) also stated that there is a positive relationship between financial system functions and economic growth in the long term.

The research on the financial inclusion impact on poverty has conducted by Dixit and Ghosh (2013) which showed that the provision of financial services access has the potential to break the vicious circle of poverty through a culture of saving and efficiency and to allow the creation of a low-cost and efficient payment mechanism. Sanjaya (2014) used regression models and panel data methods to estimate the relationship of financial inclusion with poverty in Indonesia where the result indicated that financial inclusion through micro-credit programs can improve the economic and social status of the poor.

Previous research both quantitative and qualitative methods already talked a lot about financial inclusion impact on development and poverty. Research on the impact of financial inclusion to the financial system stability is still relatively small because of limited data and understanding and not the presence of a standard proxy about the stability of the financial system itself. Several existing studies give different results which can positively impact financial inclusion and have a negative impact on the financial system stability.


Hannig and Jansen (2010) in his research stated that the provision of financial services should be prioritized to the low-income groups as it will increase the economy stability and help sustaining local economic activity. Prasad (2010) also found at the state level, financial inclusion can boost the efficiency of financial intermediation through increased domestic savings and investment which will encourage economic stability. A different result is obtained by Dupas et al. (2012), who conducted their research in western province of Kenya and displayed that the increase in banking services is not followed by financial stability improvement due the high cost of borrowing for the lower-middle class, lack of confidence, and the low quality of service.

Based on Figure 2, among seven Asian countries include South Korea, Turkey, Thailand, Malaysia, Indonesia, India, and Bangladesh, the increase in financial inclusion which is proxied to variable of ratio of the outstanding SME loans to the total of outstanding loans in the banking system (SMEL), is followed by an increase in the financial system stability, which is marked by a decline in non-performing loan (NPL). However, the increase in the value of financial inclusion, proxied to the variable of the ratio of outstanding SME loans to the total outstanding loan in the banking system (SMEL), is followed by a decreasing value of Bank z score (BZS) which means some instability may have occurred in the financial system. With the possibility of financial system instability in some Asian countries during the period 2007-2011 due to the financial inclusion programs, further research is required for Asian countries who employ financial inclusion as their strategy for inclusive growth.

Figure 2. Relationship between financial inclusion (SMEL) with financial system stability (BZS and NPL) in some Asian countries during the period 2007-2011

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More specifically, the purpose of this study is to determine the effect or impact of financial inclusion to the financial system stability in Asia. The second part of this paper will present the theory and related literature. The third part will review the data and methodology used. The fourth section will review the results and analysis, while the fifth section will give the conclusions to this paper.

II. THEORY

There are several definitions of financial inclusion. According to Bank Indonesia (2014) Financial Inclusion is the entire effort to improve people’s access to financial services by eliminating all forms of barriers, both price and non-price barriers. Hannig and Jansen (2010) define financial inclusion as an effort to include unbankable people into the formal financial system so they may have the opportunity to profit from financial services such as savings, payments and transfers. Sarma (2012) describes financial inclusion as a process which facilitate the access, availability and benefits of the formal financial system for all economic actors. Overall, it can concluded that financial inclusion is an effort to improve public access, especially unbankable member of the society to formal financial services by reducing various type of access barriers to the services.

As for the financial system stability, Asean Development Bank Institute (2014) states that there is no definitive measure or agreement applicable in general despite many attempts from institutions and researchers who have tried to form the definition based on the experience of different countries as well as previous studies. Bank Indonesia (2007) defines Financial System Stability (Stabilitas Sistem Keuangan or SSK) as a stable financial system that is able to allocate resources and to absorb any occurring shock that in order to prevent disruption to the real sector activities and to the financial system. According to the European Central Bank (2012), financial system stability is a condition in which the financial system can cope with shocks and reduce the bottlenecks in the process of financial intermediation.

There are numerous previous studies on the relationship between financial inclusion and financial system stability. The result of these studies showed both positive and negative relationship between the two variables. Khan (2011) mentioned that financial inclusion has a potential positive impact on the financial system, but the increase will not be without any risk.

Among the studies that provide evidence of the positive impact of financial inclusion to the financial system stability are the research by Morgan and Pontines (2014) and Hannig and Jansen (2010). Morgan and Pontines (2014) stated that the increase of loan for small and medium enterprises (SMEs) will increase financial stability, as shown by the downward trend of the non-performing loan (NPL) and lower risk of financial institution failure. Hannig and Jansen (2010) in his research mentioned that in addition to overcome income equality, financial inclusion also has the potential to improve financial stability, since better access for the lower economy class to formal financial institutions will strengthen the saving which later


can increase the capacity of households to cope up during vulnerable times due to the adverse effects of the crisis. Furthermore it helps to diversify the funding base of financial institutions that can reduce the shock during a global financial crisis, to increase economic resilience by accelerating growth, to facilitate diversification and to reduce poverty.

Some research examples that provide explanation of the instability risk due to the increase of financial inclusion have done by Khan (2011) and Dupas et al. (2014). In his research Khan (2011) showed that the negative impact of financial inclusion comes from the drop in credit standards as financial institutions seek to reach out the unbankable lower economy class by lowering loan terms, from the increased risk of the bank’s reputation as some countries will put a lower standards in the establishment of financial institution in order to increase financial service facilities in the rural areas, and from the instability that may occurs due to immature and insufficient regulations of microfinance institutions. While the research conducted by Dupas et al. (2014) in the western province of Kenya found that an increase in banking services does not necessarily bring any increase in financial stability if not followed by lower cost of borrowing for the lower middle economy class and better quality of service, and if the lack of confidence persists.

III. METHOD

3.1. Type and Source of Data

This paper will use data panel, which combines cross section and time series data. The panel data in the form of cross section consists of the data of seven countries in Asia: South Korea, Malaysia, Indonesia, India, Bangladesh, Thailand, and Turkey. The annual time series data comes from the period 2007-2011. These data are annual secondary data and were collected from sources such as: World Bank, World Development Indicators (WDI), the International Monetary Fund (IMF) database, and other sources. Furthermore, to provide more literature and

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insight knowledge, the author uses additional literature obtained from books, journals, and other scientific research. This research was conducted using Microsoft Excel 2010 and Eviews 8 software.

3.2. Data Analysis Method

The analytical method used in this research is both quantitative and qualitative descriptive analysis method from textbooks, journals, theses and other media as a comparison of quantitative data, where the data used in this research are secondary data. Quantitative analysis method used in this research is the method of Pearson correlation calculation and static panel data with Weighted Least Square (WLS) estimation technique and Fixed Effects Model approach. This quantitative method is selected because Pearson correlation can measure the closeness of a relationship between two variables with interval and ratio, meanwhile WLS is used so that the model will be robust against autocorrelation and heteroscedasticity problem. Qualitative descriptive analysis method is used to interpret the results of quantitative analysis.

Panel Data Analysis

The initial step in the panel data estimation is to formulate the model. Once the model is framed, the best approaching model using the Chow test, LM test and Hausman test. What follows is the criteria test that includes statistical test, econometrics test and the economic test at the end.

In analyzing the impact of financial inclusion to financial stability in Asia, the authors’ model adopts the equation used in the research of Morgan and Pontines (2014). The dependent variables are the Bank z score and non-performing loans as the proxy of financial system stability. The independent variable are the ratio of outstanding SME loan to the total outstanding loan in the banking sector as the proxy of financial inclusion, LN GDP per capita, the ratio of private credit from bank deposits and other financial institutions to GDP, current assets to deposits and short term financing, non-FDI capital flows to GDP, and financial openness index. Therefore the regression equation can be written as below:

Finstabi,t

= b1Finstabi,t-1

+b2(Fininclusioni,t )+ b3LGDPPi,t + b4CGDPi,t + b5LIQi,t +

b6NFDIi,t + b7OPNSi,t + ei,t (1)

Where:

Finstabi,t

: Proxy for the financial system stability, represented by the Bank z score (BZS) and non-performing loan (NPL) for country i in year t (BZS: Index; NPL:%)


Finstabi,t-1

: Proxy for financial system stability, represented by the Bank z score (BZS) and non-performing loan (NPL) for country i in year t-1 (BZS: Index; NPL : %).

Fininclusioni,t

: Proxy for financial inclusion, is represented by the ratio of outstanding SME loans to total outstanding loans in the banking sector (SMEL) for country i in year t (Index).

LGDPPi,t

: LN GDP per capita for country i in year t (Index)

CGDPi,t : Ratio of private credit from bank deposits and other financial institutions to

GDP for country i in year t (%).

LIQi,t

: Current assets to deposits and short-term financing for country i in year t (%).

NFDIi,t

: Non-FDI capital flows to GDP for country i in year t (Index).

OPNSi,t

: Financial openness index for country i in year t (Index).

Operational Variable Definition

1. Bank z score (BZS)

Bank z score is a score or index used to predict and assess the bankruptcy probability of a company in the future.

2. Non-performing loan (NPL)

Non-performing loans is a value that indicates the state in which the credit borrowers are not able to repay part or all of its loan obligations to the banks according to the loan term and agreement.

3. The ratio of outstanding small and medium enterprise loans to total outstanding loans in the banking sector (SMEL)

SMEL variable is the ratio of the use of deposits as loans for SMEs to the use of deposits as loans for the entire banking sector.

4. LN GDP per capita (LGDPP)

LN per capita GDP is the LN value of the average income of the population in a country. GDP per capita is obtained by dividing the national income with the population of the country.

5. The ratio of private credit from bank deposits and other financial institutions to GDP (CGDP)

CGDP variable is the ratio of loans from banks and other financial institutions to the private sector compared to GDP.


6. The ratio of current assets to deposits and short-term financing (LIQ)

LIQ variable is the ratio of the value of easily liquidated banking assets to the total deposits from the customers and short-term financing undertaken by banks.

7. Non-FDI capital flow to GDP (NFDI)

NFSI variable is the ratio of the value of foreign investment in the form of hot money to GDP. This investment value is derived from the value of capital entering the country subtracted with the value of capital leaving the country.

8. Financial Disclosure (OPNS)

Financial disclosure variables in this model uses the financial openness index issued by the Chinn-Ito. This variable indicates the degree of capital account openness towards foreign funding in a country.


4.1. Overall Description

The average value of the dependent and independent variables in seven Asian countries during the period 2007-2011 is calculated as the following:

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Table 3 shows that out of the seven Asian countries, looking from the non-performing loan (NPL) indicator, South Korea has the most stable financial system, characterized by its low ratio of NPL to GDP. This is because banks in South Korea no longer provide high-risk mortgage service to their customers. In addition, to keep the NPL value low, South Korea has a mechanism of bad loans sales by the private sector (RBA 2014). Bangladesh has the lowest level of financial stability due to its inefficiencies within banking sector management in coping with bad credit,


especially for micro-credit and agriculture (Kumar 2005). Indonesia is third in line out of seven for financial system stability category with a NPL value below the average which means that Indonesia is relatively good in financial stability.

India has the highest value on financial system stability based on the Bank z score (BZS). This is due to a good value on India’s return on banking assets stability (Murari 2012). Indonesia scores the lowest in financial system stability due to its rather volatile return on assets, compared to the other seven countries. Financial system stability measurement using either NPL or BZS is affected by several variables according to Pontines and Morgan (2014), which are GDP per capita, private credit from bank deposits and other financial institutions, banking current assets, non-FDI capital flows and financial openness.

South Korea has the highest value of the average ratio of outstanding SME loan against the total outstanding loans in commercial banks (SMEL) as a proxy for financial inclusion, which at the same time also has the lowest NPL value. India scores the lowest SMEL value, but it has the highest BZS value. This indicates that in South Korea high financial inclusion is followed high stability in the financial system, however the opposite applies in India: financial inclusion and financial system stability do not show any positive relationship.

The high average ratio of outstanding SME loan to total outstanding loan in commercial banks (SMEL) in South Korea comes from the country’s specific policies to encourage the growth of SMEs in promotion and protection of SMEs, direct assistance for SMEs, as well as its domestic-demand-oriented policy (Kim 2006). India has a lower SMEL due to the barriers for SMEs to access formal financial services. One of the major obstacle is the high cost of borrowing in a formal financial institution. The majority of SMEs (93%) borrow from non-formal financial institutions or from private funds, and in average theses SME entrepreneurs possess a rather poor management skill and have a very limited capital (ADBI 2014).

Among these seven countries, South Korea, which has the lowest NPL, also has the highest average GCP per capita during 2007-2011, with Bangladesh, which has the highest NPL, is also the country with the lowest GDP per capita. The GDP per capita of Indonesia is relatively low but it also possess a relatively low NPL. This indicates that among the sample countries, the countries with high GDP per capita most likely have a good financial stability, and vice versa.

Thailand scores the highest ratio of private credit from bank deposits and other financial institutions to GDP (CGDP). This is the result of the low barriers to profit from financial services such as credit. This is supported the research conducted by Camara and Tuesta (2014), in which Thailand ranks 20 out of 82 countries in the category of the absence of barriers for the community members to benefit from formal financial services. The lowest CGDP is owned by Indonesia due to its credit constraints which remain high and to its high number of unbankable people, at the level of 80% of the total population aged 15 years and over (ADBI, 2014). Both Thailand and Indonesia, which has the highest and lowest CGDP, a relatively low BZS and a relatively high NPL.


The highest average ratio of banking current assets to deposits and short-term financing (LIQ) is owned by Malaysia due to its high value of banking current assets (World Bank 2013). Malaysia also has a relatively good level of financial system stability illustrated by its low NPL and high BZS. Indonesia itself ranks second with a relatively high value of LIQ, but with a relatively low level of financial system stability. South Korea has the lowest LIQ due to higher amount of deposits and short-term funding (World Bank 2013). Korea has a relatively good financial system stability in account of its low NPL, but it has a slightly below than average BZS.

Bangladesh ranks first for the ratio of non-FDI capital flows to GDP per capita (NFDI). This is because the non-FDI capital inflow of Bangladesh is larger than its outflow (IMF 2014). Bangladesh has a relatively low financial system stability. The lowest NFDI is by Turkey where its non-FDI capital outflow is larger than the inflow. Turkey has a good stability of financial system from its BZS indicator value although it has a relatively high NPL. Indonesia ranks third with a high NFDI ratio value and a relatively low stability from BZS point of view.

The last variable is the financial disclosure (OPNS). Indonesia notches the highest levels of OPNS due to its high capital account openness towards foreign funding (Chin and Ito 2007). Indonesia has a relatively low stability of financial system. India and Bangladesh have the lowest OPNS value, but their financial stability scores are on the opposite side. India has high BZS and low NPL, which means that stability of India’s financial system is relatively good. On the other side, Bangladesh has a lower-than-average BZS and a high NPL such as Indonesia. This indicates within the sample countries, the value of OPNS does not bring any consistent condition difference on the financial system stability, especially within the three countries above.

4.2. Relationship between Financial Inclusion and Financial System Stability

Based on the data obtained which consist of Bank z score, NPL, and the ratio of outstanding SME loan against the total outstanding loan in commercial banks (SMEL), the author find the Pearson correlation coefficient as the following:

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Table 4 shows a mismatch correlation between the SMEL ratio as a proxy for financial inclusion (SMEL), with the non-performing loan (NPL), as a proxy for the financial system stability. This may imply that the increase in the value of SMEL ratio as a proxy for financial inclusion might


be followed by a decrease in non-performing loans, and vice versa. The correlation between these two variables is weak with a correlation value at the level of 0.2<|r|<0.399 (Sugiyono 2003). This correlation value is not significant at the 5% level based on the test of significance because the value of |t-count| (-1458) ≤ t-table (1.96).

The negative correlation also present between the rate of outstanding SME loan to total outstanding loan in commercial banks with the Bank z score, which is also a proxy for the financial system stability. This may imply that the increase in the value of outstanding SME loans to the total outstanding loans in commercial banks as a proxy for financial inclusion might be followed by a decline in the value of Bank z, and vice versa. The correlation between these two variables is moderate where the correlation value is at the level of 0.4<|r|<0.599 (Sugiyono 2003). The value is significant at the 5% level due to |t-count|(-4.069) ≥ t-table (1.96).

4.3. Factors Affecting Financial System Stability

To formulate the answer on the factors that affect the financial system stability in Asia during the period of 2007-2008, there are four models developed in order to get the best model formulation. The first model uses Bank Z score as a proxy for financial system stability and inserts a lag independent variable. The second model uses Bank Z score as a proxy for financial system stability but does not include a lag independent variable. The third model uses NPL as a proxy for financial system stability and inserts a lag independent variable. The last model uses NPL as a proxy for financial system stability, but does not include a lag independent variable.

Once built, the four models are tested to find the best panel data approach model. This is determined through several tests: the Chow test to choose between Pooled Least Square (PLS) or Fixed Effect Model (FEM), the Hausman test to choose between FEM or Random Effects Model (REM), and the Lagrange Multiplier (LM) test to choose between REM or PLS.

The first one is Chow Test. From the Chow test, the four models show a probability of (0.0000)<α(0.05), which means to reject H0, so the models will use the fixed effect model. The next step is Hausman test. The result of this test indicates the probability of (0.0000)>α(0.05), means to reject H0, so the models will use the fixed effect model. After the result of Chow and Hausman test, LM test is no longer necessary, because the two previous tests show that the resulting model is a fixed effect model.

Based on the statistical test using regression models with the Fixed Effect Model approach and the weighted least squares estimation methods in order to overcome the heteroscedasticity problem, a robust regression model is obtained. The estimation results of the four models are shown in Table 5 as follows:


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Statistic test is then conducted once the best model is acquired. Based on the t-test of each variable, the F-test, and the adjusted R-squared value, the first model presented in Table 5 is the best model compared to the three other models. The first model has a significant F-test at the level of 1% with the highest value of adjusted R-square compared to the three other models as well as a significant t-test on the five variables.

The next step is econometric measurement in order to meet the classic assumption test including normality test, multicollinearity test, autocorrelation test, and heteroscedasticity test. If these classical assumptions are met, the obtained estimate would fulfill the BLUE (Best Linear Unbiased Estimator) assumption criteria, where the estimate will be unbiased, consistent, and efficient. Based on the four tests result, the authors obtained the normality test of Jarque Bera probability at (0.7892)>α(0.05), which means that the H0 is not rejected and the normality assumption is met. In the multicollinearity test, the correlation between the dependent variable is at the level of 0.003131<|r|<0.750298 where |r|<0.8 so according to Gujarati (2008) this model is free from the multicollinearity problem. In addition the VIF value is between 2.6 to 8.6 which also illustrate the absence of multicollinearity problem. Furthermore, in the autocorrelation test, the value of Durbin h is at -0.4925, meaning that the value of |Durbin h|<Z α/2 (1.96), therefore the H0 is not rejected or in other word autocorrelation does not exist. Regarding heteroscedasticity problem, the model in Table 5 is already robust due to the use of weighted least squares estimation method.

Based on the statistical and econometric testing, the model in Table 5 is the best model to explain the impact of financial inclusion, as well as other factors, on the financial system stability. Table 5 shows that the variables that significantly affect the financial system stability


are the stability of the financial system in the previous period (AR (1)), the ratio of outstanding SME loans to total outstanding loans in commercial banks (SMEL), GDP per capita (LNGDPP), the ratio of current assets to deposits and short-term funding (LIQ), and non-FDI capital flows to GDP (NFDI). On the other hand variables with insignificant effect are including the ratio of private credit to GDP (CGDP) and the Financial Openness (OPNS).

Financial System Stability in the Previous Period (AR (1))

The stability of the financial system in period t-1 (AR(1)) has a positive and significant relationship at the level of 1% with the financial system stability. Any increase in AR(1) will increase the financial system stability. Based on the estimation, an increase of 1 unit on the stability of the financial system in period t-1 will improve the stability of the financial system in period t by 0.41 unit, ceteris paribus. This indicates a strong influence of the financial system stability in the previous period to the financial system stability in the period-t. The research of Morgan and Pontines (2014) also yield similar result at the significance level of 1%.

Based on the elasticity calculation, the financial system stability in period t-1, as a variable, has an elasticity of 0.394. This means an increase of 1% in the financial system stability in period t-1 will improve the financial system stability in period-t by 0.394%, ceteris paribus. The elasticity in the variable of financial system stability in period t-1 has the largest value compared to other variables that significantly affect the stability of the financial system in period-t, therefore it can be concluded that the variable of financial system stability in period t-1 gives the greatest influence to the value of financial system stability in period-t.

The ratio of outstanding SME loans to total outstanding loans in Commercial Bank (SMEL)

The SMEL ratio has a positive and significant relationship at the level of 1% with the financial system stability. Based on the estimation, an increase of 1 unit on financial inclusion will improve the financial system stability by 10.33 unit, ceteris paribus. The reason is that improvement in SMEL is related to improvement in the real sector. In addition, the increase in SMEL will be followed by a strengthening in SMEL deposit base of SMEs that could be used to improve the process of intermediation (Khan 2011). The study by Morgan and Pontines (2014) on the relationship between financial inclusion and financial stability in middle-income countries also gives the same result at the level of 10%.

Based on the elasticity calculation, financial inclusion has an elasticity of 0.184 which means that an increase of 1% on financial inclusion will increase the stability of the financial system by 0.184%, ceteris paribus. Elasticity value on financial inclusion is a relatively high and ranks second only to the elasticity value of the financial system stability in period t-1. It can be


concluded then that sufficient financial inclusion can affect significantly the financial system stability.

GDP per capita (LNGDPP)

GDP per capita (LNGDPP) has a positive and significant correlation at the level of 5% with the financial system stability. Based on the estimation, an increase of 1% in GDP per capita will increase the financial system stability by 10.33 unit, ceteris paribus. This is due to the increase in GDP per capita which would lead to an increase in the number of formal accounts in banking sector (Allen et al 2012). This will be followed by increase of deposit base and improvement of the intermediation process. The research by Morgan and Pontines (2014) also gives similar result with at the significance level of 1%.

Based on the elasticity calculation, GDP per capita has an elasticity of 0.086, meaning than an increase of 1% in GDP per capita will increase the financial system stability by 0.086%, ceteris paribus. The elasticity of the GDP per capita ranks fourth out of the five variables that significantly affect the financial system stability.

Non-FDI Capital Flow to GDP (NFDI)

Non-FDI capital flows to GDP (NFDI) has a positive and significant correlation at the level of 5% with the financial system stability. Based on the estimation, an increase of 1% in non-FDI capital flows to GDP will improve the financial system stability by 0.00000067 unit, ceteris

paribus. This positive relationship based on the account that NFDI will increase the amount of bank deposit and thus increasing the credit. Furthermore, NFDI can increase foreign exchange reserves to the country where the foreign capital is flowing into (Lane 2006). These results have similarities with the results of study conducted by Lane (2006).

Based on the elasticity calculation, non-FDI capital flows to GDP has an elasticity of 0.006, meaning that an increase of 1% in non-FDI capital flows to GDP will improve the financial system stability by 0.006%, ceteris paribus. The elasticity of the non-FDI capital flows to GDP has the smallest value among the five variables that significantly affect the financial system stability. Therefore we can conclude that the variable of non-FDI capital flows to GDP is the least impactful to the financial system stability compared to the other four other variables.

The Ratio of Current Assets to Deposits and Short-Term Financing (LIQ)

The ratio of current assets to deposits and short-term funding (LIQ) has a positive and significant correlation at the level of 5% with the financial system stability. Based on the estimation, an increase of 1% in the LIQ will increase the financial system stability by 0.12% unit, ceteris


paribus. This indicates that an increase in LIQ will improve the financial system stability as greater current assets will give customers more confidence to their bank when a shock takes place (Morgan and Pontines 2014). Morgan and Pontines (2014) also gives similar results in their study at the significance level of 1%.

Based on the elasticity calculation, the LIQ variable has an elasticity value of 0.16. This means an increase of 1% on LIQ will increase the financial system stability by 0.16%, ceteris

paribus. The elasticity value of LIQ variable ranks third among the five variables that significantly affect the financial system stability.

V. CONCLUSION

This paper brings two conclusions at the very least: first, the correlation between financial inclusion using Bank Z score and financial system stability in Asia, as the indicators, shows an intermediate level of the relationship. Secondly, the factors that significantly affect the financial system stability (BZS) in Asia based on seven sample countries’ data in the period 2007-2011 include the financial system stability in the previous period (AR(1)), financial inclusion (SMEL), GDP per capita (LNGDPP), non-FDI capital flows to GDP (NFDI), and the ratio of current assets to deposits and short-term funding (LIQ). These five variables have a positive and significant relationship to the financial system stability (BZS).

Referring to the above conclusion, several implications and suggestions can be given out. First, the government, particularly the governments of Bangladesh, India, and Indonesia should boost their GDP per capita as it will increase the financial system stability during that period the period to come. Furthermore, the government of Turkey, South Korea, and Thailand should encourage the inflow of portfolio investments in order to improve the financial system stability during that period and the period to come. Second, the banking sector, in particular Malaysia, India, and Bangladesh should promote financial inclusion in order to increase the financial system stability in that period and the period to come. But for India, the increase in financial inclusion must be followed by eliminating the barriers in accessing formal financial services as well as improving risk management in the banking sector to avoid any potential instability. In addition, the banking sector in South Korea, India, and Turkey should encourage an increase in current assets of banking sector in order to improve the financial system stability in that period and the period to come. For further study in this topic, this paper recommends more in-depth analysis on the relationship between financial inclusion and financial system stability based on income levels between countries.


RefeRences

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407Macroeconomics Indicators and Bank Stability: A Case of Banking in Indonesia

MACROECONOMICSINDICATORS AND BANK STABILITY: A CASE OF BANKING IN INDONESIA

Norzitah Abdul Karim1

Syed Musa Syed Jaafar Al-Habshi2

Muhamad Abduh3

This paper provides new empirical evidence of the bank stability in relation to the macroeconomic

indicator of Indonesia. The bank stability is first calculated using Z-score, and then regressed using

Autoregressive distributive lag (ARDL) model on the macroeconomic variables i.e. Gross Domestic Product

(GDP) in US dollar, Interest rates (IR) in percentage and Consumer Price Index (CPI). To analyse further the

long run relationship and the impact of bank stability, Cholesky standard deviation shock to the model,

ARDL and Impulse Response Function (IRF) are used. These ARDL and IRF are carried out independently and

repeated over data for three different models: (i) the commercial banks model, (ii) Islamic banks model,

and (iii) the overall banking industry model. The empirical findings suggest long run relationship between

the stability of commercial banks and macroeconomic factors. The findings also suggest the long run

relationship between the stability of overall banking industry and macroeconomic factors. However, there

is no evidence of long run relationship between the stability of Islamic banks and macroeconomics factors.

Nevertheless, this finding is subject to the limitation of data, on the number of Islamic banks included in

the test. The sample of Islamic banks was 5 banks from a total of 10 Islamic banks, due to insufficient

data, as compared to the larger number of commercial banks taken into, as the sample.

Abstract

Keywords: Bank Stability, Z-score, ARDL, Commercial Banks, Islamic Banks

JEL Classification: E44, E63, G21

1 Universiti Teknologi MARA, IIUM Institute of Islamic Banking and Finance (IIiBF), International Islamic University Malaysia2 IIUM Institute of Islamic Banking and Finance (IIiBF), International Islamic University Malaysia3 School of Business and Economics, Universiti Brunei Darussalam


I. INTRODUCTION

The recent global financial crisis has induced a series of failure of many conventional banks and led to an increased interest in the Islamic banking. The financial crisis also calls for a financial system that is stable throughout all time and not affected by any crisis. The issue on the financial stability and bank stability has always been the interest of all central banks around the world. It is paramount important of the sustainability of the banking industry itself. Thus, with the parallel players of Islamic and conventional banks, a comparison between the two is inevitable. According to Hasan & Dridi (2010), Bourkhis & Nabi (2013), Parashar and Venkatesh (2010), the performance and stability of the Islamic banks are better than conventional banks, for the period after and during the crisis. Parashar and Venkatesh (2010) also noted that Islamic banking is safer than conventional banks due to its characteristics including its product structure that is asset backed. In contrast, Beck et al. (2013) found Islamic Banking are less cost-effective but higher intermediation and better capitalized than the conventional banking, in the normal economic.

This paper focuses at the bank’s stability in Indonesia. It compares the stability of Islamic banks, commercial banks, and overall banking industry using Z-score4. It explores the Z-score as the indicator of bank’s stability in Indonesia. A data from BankScope5 is obtained to include 58 commercial banks and 5 Islamic banks in Indonesia from 1999 to 2013. The bank’s Z-score and independent variables such as Gross Domestic Product (GDP) in US dollar, Interest rates (IR) in percentage and Consumer Price Index (CPI) are regressed using Autoregressive distributive lag (ARDL) model and later a shock to the model is analysed using the Impulse Response Function (IRF). These procedures are carried out independently and repeated for 3 models for commercial banks, Islamic banks, and Indonesia banking industry.

The remaining of this paper is structured as follows. Section 2 discussed the development of the Z-score as a measure of bank stability, calculation of Z-score and the effect of macroeconomics factors on bank stability. Section 3 looks at the data and methodology. Section 4 discusses at the findings and lastly the conclusion is drawn in section 5.

II. THEORY

2.1. The Z-score as a measure of Bank Stability

Due to the recent global financial crisis, it has become a great interest and draw enormous attention to the bank insolvency risk (Rahman, 2010) thus, the Z-score increased its important than ever (Strobel, 2011). (Rahman, 2010) also noted that there are 3 other market-based-risk

4 Z-score is a measure of the distance-to-default and inversely related to the probability of a bank’s insolvency (Rajhi & Hassairi, 2013). Higher score of z-score indicates a more stable bank than the lower score.

5 The main data source is BankScope database produced by the Bureau van Dijk. BankScope reports the data in the original currencies of the respected dual banking countries and provides a choice to convert data in any other currencies, including the US Dollar. (Hassan et. al., 2009). The bank specific data was converted into US Dollar.


measuring methods: Z-score, CAPM risk measures, and Zrisk index with the rationale that it is the most appropriate measure because Malaysian Islamic banks are relatively small and not publicly traded on the stock exchange. However, a careful look at the formula of Zrisk index will reveal that it is very much similar to Z-score. The empirical evidences of Z-score as a proxy of bank stability are compiled in table 1 below.

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2.2. Macroeconomics Effects on the financial and bank’s stability

Previous researches like Sufian & Habibullah (2012), Köhler (2014), Bourkhis & Nabi (2013) and Cihák & Hesse (2007) have used macroeconomic factors as the control variables in explaining the variations in the response variables. Sufian & Habibullah (2012), examined the effects of bank specific characteristics and macroeconomic factors on the bank’s performance. These macroeconomics factors include gross domestic product and inflation. Similarly, Bourkhis & Nabi (2013) examined the bank’s soundness using Z-score and look at the macroeconomics factors such as GDP growth, inflation and exchange rate as some of the explanatory variables.


In addition, Cihák & Hesse (2007) in their research adjusted the of the macroeconomic cycle by including control variables from macroeconomic variables such as GDP growth, inflation, interest rate, and exchange rate appreciation. Diaconu & Oanea (2014) investigated factors influencing the bank stability using Z-score, and employed 4 variables: inflation, gross domestic products, BET rate, and interbank offering rate for 3 months. The relationships between these macroeconomics variables and bank or financial stability are discussed in table 2 below.

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2.3. ARDL and ECM

Abduh & Omar (2012) and Abduh (2013) used ARDL to investigate the short run and long run relationship between: (i) stock market and economic growth, and, (ii) Islamic banking and economic growth, respectively. The ARDL model consists of an autoregressive part and a regression with distributed lags over a set of other variables. The ARDL model regresses a variable over its own past plus the present and past values of a number of exogenous variables (Abduh & Omar, 2012). Nevertheless, the ARDL method excludes pre-testing variables, because


as highlighted in numerous literatures, problem of unit root-cointegration exists where the power of the unit root tests is typically very low and there is a switch in the distribution function of the test statistics.(Abduh & Omar, 2012). The ARDL approach is to test the existence of a relationship between variables in levels is applicable regardless the underlying regressors are purely I(0), purely I(1), or mixed (Abduh & Omar, 2012). Without having any prior information about the direction of the long-run relationship among the variables, the ARDL approach to cointegration involves estimating the conditional error correction (EC) version of the ARDL (Abduh & Omar, 2012).

III. METHODOLOGY

The data gathered from BankScope, a global database on various types of banking. There are a total of 60 commercial and 10 Islamic banks in Indonesia in 2014. However, only banks with at least two observations are included. Finally, we only included 58 commercial and 5 Islamic banks due to insufficient data. Meanwhile, the macroeconomic data are obtained from the World Bank Reports (World Development Indicators). The banking data and macroeconomic data are annual data for the period from 1999 to 2013. First, the measurement of bank’s stability is measured using Z-score and calculated using the well-used formula,

Z = (ROA + CAP) / σROA.

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The descriptive statistics is presented in the Table 3 below.


From table 3 above, it is noted that the Z-score of conventional banks and whole banking industry have similar trends in the movement. This is understandable as conventional banks represent majority of banks in the whole banking industry. From 1999 to 2013, the Islamic banks are more stable than the conventional banks and the whole banking industry, given higher Z-score, in general. According to Rajhi & Hassairi (2013) the Z-score is a measure of the distance-to-default, thus, higher Z-score increases the bank’s distance to default, hence more stable the bank will be. However, this higher Z-score is with the exception on year 2000 and 2007. It should be noted that the year 2001 is the end of Asian Financial Crisis, for Indonesia, whereas year 2007 is the beginning of Global Financial Crisis or also known as systemic crisis (Laeven & Valencia, 2013). Thus, crisis affected the Islamic banks, either later or first than the conventional banks or the whole banking industry. Interestingly, in 2006, prior to Global Financial Crisis, high Z-score was consistently reported across all Islamic, conventional and the whole banking industry.

Once, the bank’s stability is established, the unit root test is then carried out using Augmented Dickey Fuller and Phillip Peron tests for all the four variables to ensure that these economic time series do not have unit root and stationary. These tests for stationary are carried out with and without intercept at level and first difference. Upon completion of these tests, the Z-score of commercial banks (ZC), Z-score of Islamic Banks (ZI), Z-score of banking industry (ZALL) and independent variables such as Gross Domestic Product (GDP) in US dollar, Interest rates (IR) in percentage and Consumer Price Index (CPI) are regressed using Autoregressive distributive lag (ARDL) model, and later a shock to the model is analysed using the Impulse Response Function (IRF). These processes are replicated over for 3 different models, that is, firstly, to test the bank’s stability of commercial banks with the macroeconomic variables, secondly to test the bank’s stability of Islamic banks with the macroeconomic variables and finally, to test the bank’s stability of overall banks(banking industry in Indonesia) with the macroeconomic variables.

The models initially tested are

ZALLt = β0 + β1GDPt + β2IRt+ β0CPIt + εt (1)

ZIt = β0 + β1GDPt + β2IRt+ β0CPIt + εt (2)

ZCt = β0 + β1GDPt + β2IRt+ β0CPIt + εt (3)

where ZALLt is the Z-score (bank stability) of banking industry in Indonesia; ZIt is the Z-score (bank stability) of Islamic Banks in Indonesia; ZCt is the Z-score (bank stability) of commercial banks; GDPt is Gross Domestic Product; IRt is the Interest rates and CPIt is the Consumer Price Index, while εt is error term.

Pesaran, Shin, & Smith (2001) suggested a bound testing method with the equation of any long-run relationship may be given by the following equations:


(Equation 4 for Industry)

(Equation 5 for Commercial Banks)

(Equation 6 for Islamic Banks)

where p is the optimal lag length and D refers to the first difference of variables.

Finally, an analysis on the shock upon the variables are conducted. An impulse response functions using Cholesky one standard deviations traces the effect of a one-time shock to one of the innovations on current and future values of the endogenous variables.


4.1. Test for Unit Root

The test for unit root and non-stationary are carried out for all variables employed in the model : (i) Z-score of commercial banks (ZC), (ii) Z-score of Islamic Banks (ZI), (iii) Z-score of banking industry(ZALL), (iv) gross domestic product (GDP), (v) interest rates(IR), and (vi) consumer price index (CPI) using Augmented Dickey Fuller (ADF) and Phillip Peron (PP) tests for stationary with and without intercept at level and first difference. Table 4 shows that variables like Z-score for Commercial Banks, Islamic Banks and Industry in Indonesia, Gross Domestic Product, and Interest Rates are all non-unit root and stationary at a significance level of 1% but at first difference for tests under ADF and PP. For CPI, it does not unit root problem and stationary at a significance level of 5% for both ADF and PP tests for stationary.


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4.2. Commercial Bank’s Stability and Macroeconomic Variables

The results for overall banking industry is displayed in Table 5 and 6. Based on Table 5, the optimal model can be selected using the model selection criteria like Schwartz-Bayesian Criteria and (SBC) and Akaike Information Criteria (AIC), where the AIC is -1.35 and SBC is -0.83. The optimal derived is at first difference and at lagged equal to 1. All the coefficients of the variables are significant at least 5% significance level except first difference of Interest rate. The model above can be rewritten as:


Table 6 shows the value of F-statistic of 1655.751, and the values of (k + 1) = 4 variables which are Z-score (ZC), (Gross Domestic Product (GDP), Interest rates (IR), and Consumer Price Index (CPI)) in our model. Thus, for the Bounds Test tables of critical values, the value of is k = 3. To ascertain the critical values, the Table CI (iii) of Pesaran et.al (2001) is used since there is no constrain on the intercept of the model and no linear trend term. The lower and upper bounds for the F-test statistic at the 10%, 5%, and 1% significance levels are [2.72 , 3.77], [3.23 , 4.35], and [4.29 , 5.61] respectively. It is noted that the F-statistic exceed the upper bound at the 1% significance level. Thus, it is concluded that there is evidence of a long-run relationship between the four time-series at 1% significance level.

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An Impulse response function (IRF) as shown in figure 1 above revealed that a shock of one standard deviation Cholesky to GDP, IR and CPI on the Z-score of commercial banks reach its equilibrium after year 6. Both GDP and CPI reported a positive response to the shock in the short run as compared to a negative shock for IR. This prediction confirmed to the previous empirical findings that GDP and price stability have positive relationship. Similarly the previous findings on interest rate reaffirmed that higher interest rates causes instability among commercial banks as depicted by blue line as negative.

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Figure 1. Response to Cholesky One S.D. Innovations + 2 S.E


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4.3. Islamic Bank’s Stability and Macroeconomic Variables

The results for overall banking industry is discussed in Table 7 and 8. Based on Table 7, the optimal model can be selected using the model selection criteria like Schwartz-Bayesian Criteria and (SBC) and Akaike Information Criteria (AIC), where the AIC is 5.97 and SBC is 6.5. The optimal derived is at first difference and at lagged equal to 1. All the coefficients of the variables are not significant even at 10% significance level. The model above can be rewritten as:


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From the Table 8 above, the value of F-statistic is 1.498, and the values of (k + 1) = 4 variables which are Z-score (ZI), (Gross Domestic Product (GDP), Interest rates (IR), Money Supplies (M2) and Consumer Price Index (CPI)) in our model. Thus, for the Bounds Test tables of critical values, the value of is k = 3. To ascertain the critical values, the Table CI (iii) of Pesaran et.al (2001) is used since there is no constrain on the intercept of the model and no linear trend term. The lower and upper bounds for the F-test statistic at the 10%, 5%, and 1% significance levels are [2.72 , 3.77], [3.23 , 4.35], and [4.29 , 5.61] respectively. It is noted that the F-statistic is smaller than the lower bound at the 10% significance level. Thus, it is concluded that there is


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no evidence of a long-run relationship between the Z-score and all the three variables at 10% significance level. This suggests that the stability Islamic banks in Indonesia is not affected by the macroeconomic factors but rather could be affected by the real economic activities itself.

A shock of one standard deviation Cholesky to GDP and CPI on the Z-score of Islamic banks revealed negative response, as shown in figure 4.2 above. Negative response for GDP and CPI are contrary to the previous empirical result. However, IR reported a positive response to the shock and hence this is also contrary to the previous empirical result. It is also noted that the equilibrium is only reach later after year 8 for GDP and CPI whereas IR seem to be later than year 10.

4.4. Indonesian Banking Industry’s Stability and Macroeconomic Variables

The result for overall banking industry is discussed in Table 9 and 10. Based on Table 9, the optimal model can be selected using the model selection criteria like Schwartz-Bayesian Criteria and (SBC) and Akaike Information Criteria (AIC), where the AIC is 2.39 and SBC is -2.91. The optimal derived is at first difference and at lagged equal to 1. Only the coefficient of interest rates variable is significant at 10% significance level. The model above can be rewritten as


From Table 10, the value of F-statistic is 28.493, and the values of (k + 1) = 4 variables which are Z-score (ZALL), (Gross Domestic Product (GDP), Interest rates (IR), and Consumer Price Index (CPI)) in our model. Thus, for the Bounds Test tables of critical values, the value of is k = 3. To ascertain the critical values, the Table CI (iii) of Pesaran et.al (2001) is used since there is no constrain on the intercept of the model and no linear trend term. The lower and upper bounds for the F-test statistic at the 10%, 5%, and 1% significance levels are [2.72 , 3.77], [3.23 , 4.35], and [4.29 , 5.61] respectively. It is noted that the F-statistic exceed the upper bound at the 1% significance level. Thus, it is concluded that there is evidence of a long-run relationship between the four time-series at 1% significance level.

From figure 3, a shock of one standard deviation Cholesky to GDP, IR and CPI on the Z-score of overall banking industry revealed that most of the shocks reach its equilibrium after year 8. Both GDP and CPI reported a positive response to the shock in the short run as compared to a negative response to the shock for IR. This prediction confirmed to the previous empirical findings that GDP and price stability have positive relationship. Similarly the previous findings on interest rate reaffirmed that higher interest rates causes instability among banking industry as depicted negative by blue line.

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V. CONCLUSIONS

The ARDL models for commercial and overall banking industry show similar findings with the evidences for long run relationship between the stability (of both commercial banks and the whole banking industry) and the macroeconomic factors, as shown in the bound-test. The IRF on both models also reveal almost similar findings that confirming to the previous empirical results. The reasons of similar findings for both commercial and overall banking industry are the samples of commercial banks are 58 banks from 60 commercial banks. These commercial banks are in fact the majority players in the Indonesia banking industry.

As for the Islamic banks, it is concluded that the ARDL model found no evidence of a long-run relationship between the Z-score of Islamic banks and macroeconomic factors at 10% significance level. This suggests that the stability Islamic banks in Indonesia is not affected by the macroeconomic factors but rather could be affected by the real economic activities itself. The limitation of the analysis is on the number of Islamic banks included in the test as 5 banks from a total of 10 Islamic banks, due to insufficient data.


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425Interest Rate Metric System: Alternative Strategy for Banking Industry

INTEREST RATE METRIC SYSTEM:ALTERNATIVE STRATEGY FOR BANKING INDUSTRY

Stephanus Ivan Goenawan1

The financial transaction facilities including Automated Teller Machine (ATM), mobile banking, or

internet banking can help customers to make real time transactions across location and time zones. On

the basis of these two facts, this research comparatively analyze and prove that the daily interest rate

system as commonly practiced by the bank potentially create loss to them. Since the daily interest rate

system is based on the change of the date, the customers can double the nominal interest rate income.

Using comparative analysis, this paper show that the potential loss may be prevented when the bank use

the metric interest rate system, which is based on the time in seconds.

Abstract

Keywords: Interest Rate System, Metric System, Interest Rate Policy

JEL Classification: G21, G28, G38

1 Stephanus Ivan Goenawan is the creator of the metric system and lecturer on Department of Industrial Engineering, Atma Jaya University ([email protected]).


I. INTRODUCTION

Real time financial transaction process by modern banking is no longer a must in the global era. The real time financial transaction process can be also done for customers living in different areas with different time horizon. Of the above statement, it implies the existing potential risk due to daily-based interest rate system currently implemented by banks. According to Goenawan (2013), there are four factual conditions of banks that implement current deposit rate system of conventional bank.

1. Banks are currently implementing daily-based interest rate system in which every single transaction recorded in the account would be marked by the changes of date, and the total amount would be delivered and printed once in a month.

2. Financial transfer process can be done online through several channels such Automatic Teller Machine (ATM), Mobile Banking, Mobile Phone Bill, Internet Banking, even they are not fully real time. The meaning of real time is deliverables process of money that can be settled real time with the few seconds time lag.

3. The transferred fund would be received and recorded based on the time and date the money is transferred. The time and date between transferor and transferee must be different in which it can be forward or backward depending on geography or location the sender transfers the money and the beneficiary receives it.

4. The transaction can be done online across locations all over the globe. The transaction with the same provider and the same currency can be real time and may not be charged by additional transaction fee.

According to Devie (2000), daily-based interest rate system of conventional bank is granted interest rate based on differences of the date. Thus in the case of changes in dates, then customers would have the claim upon the daily interest rate.

Therefore, if it takes deeper look on the above four factual conditions, then there is fatal vulnerability of the daily-based interest rate system as it leads to potential losses banks and customers would be suffering from. The potential losses is the potential multiple interest rate the customers could earn more than they actually deserve from the bank. From the customer’s side, the potential losses could lead to unfair services performed by the banks.

II. THEORY

In the age of advance information technology, it is easier to conduct real time transaction. According to Bank Indonesia, there are several types of real financial transaction or real time online (RTOL) such as transfer through Automatic Teller Machine (ATM), mobile phone bill, mobile banking, or internet banking. Real time financial transaction (RTOL) is a financial transactions that are interconncted each other with only few seconds of time lag between


sender and beneficiary, especially for financial transactions between customers using the same provider (bank).

2.1 Time Horizon

National Time Horizon

In the age of globalization, all information can be interconnected real time through communication channel such as mobile phone or internet network. The interconnected information means any information can be directly spread worldwide. Despite the speed of information flow, it cannot be denied that set of time and date is different between a region and the others. In the case of Indonesia, there are three horizons of time which are Western Indonesian Time, Central Indonesian Time, and Eastern Indonesia Time (bmg.go.id)

Banda Aceh

Medan

Padang

Jambi

Palembang

Bengkulu

Bandar Lampung

TanjungPinang

PangkalPinang

Serang

Jakarta

Bandung

Semarang

Yogyakarta

Surabaya

Denpasar

Pontianak

PalangkarayaBanjarmasin

Samarinda

Mataram

Kupang

Manado

GorontaloPalu

Makassar

Mamuju

Kendari

Ternate

Ambon

Manokwari

Jayapura

Pekanbaru

PEMBAGIAN WILAYAH WAKTU DI INDONESIAKEP.PRES. NO. 41 Th. 1987 BERLAKU MULAI 1 JANUARI 1988

Figure 1. Time Horizons in Indonesia

International Time Horizon

The international time horizon as exhibited by Figure 2 below shows that time horizon is actually more than 24 hours around the globe. Besides, the difference between time horizons is not always 1 hour as there are number of regions with 30 minutes time lag, even just 15 minutes. Some of the northern and southern regions in the middle of Pacific Ocean could have 24 hours’ time lag which means the two regions have the same time at two different days. Moreover there are also two extreme regions located in Pacific Ocean with 26 hours’ time lag. It implies


I hour time lag could create three different dates. For instance Monday 10.30 UTC in London is at the same time with Tuesday 00.30 (UTC+14) in Line Island and Sunday 23.30 (UTC-11) in Samoa. Moreover Indonesia’s UTC is divided into three time horizons which are Western Indonesia Time (Waktu Indonesia Barat/WIB) which is UTC+7, Central Indonesia Time (Waktu Indonesia Tengah/WITA) which is UTC+8, and Eastern Indonesian Time (Waktu Indonesia Timur/WIT) which is UTC+9 (en.wikipedia.org).

Figure 2. Global Time Horizons

2.2. Interest Rate of Conventional Savings

In general, there are two approaches of interest rate system implemented by conventional banks. According to Goenawan (2013) those systems are interest rate system based on the average daily balance and interest rate system based on daily balance. There are number banks that set 365 days in a year, while others actually set 366 days (leap year), 365¼, or 360 days. Before it comes to the implementation of average balance-based interest rate system and balance-based interest rate system, the following Table 1 exhibits loan and debt on a daily basis to help explain the calculation.


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Notes:

p = transaction index of the time limit where interest rate final calculation of the savings is printed (printout). In general, the value of p would end up within one month (29, 30, or 31 days).

i = variable of days with range of days started from zero (0) until p.

Hi = size of the accumulation of final credit (+) or debt (-) at the period (day) i.

∆hi = range of periods (days) between Hi to Hi+1.

The Calculation of Average-based Daily Interest Rate

According to Devie (2000) it is necessary to figure out daily balance within a month (SRH) before calculating average-based daily interest rate. The average-based daily interest rate is obtained through the following formula.

r = ∆h1 + ∆h2 + .... + ∆hp (1)

SRH = (H0 × ∆h1 + (H0+H1) × ∆h2 + .... + (H0+H1+...+Hp-1) × ∆hp) / r (2)

BRH = SRH × r × α (3)

where α is daily interest rate based on daily balance within one month (SRH) which is commonly known as annual interest rate that need to be divided by the number of days within a year (1 year = 365 days); r is the accumulation of number of days ∆hi to ∆hp. In general, for one month, r = 29, r = 3-, r = 31; SRH is average daily balance within period range of p; and BRH is average daily interest rate earned within period range of p.

The Calculation of Daily Interest Rate System

According to Devie (2000), the calculation of interest rate is conducted in a daily basis within a month. Prior to that, there are several points to highlight regarding the formula of daily interest rate calculation.


αi is basically daily interest rate based on balance at period i which is commonly known as annual interest rate divided by the number of days within a year (1 year = 365 days).

BSH = H0×∆h1×α1 + (H0+H1)×∆h2×α2 + .... + (H0+H1+...+Hp-1)×∆hp×αp (4)

Both of those daily-based systems are widely-used by conventional banks. It those conventional banks do not use balance to determine the interest rate or progressive interest rate (α = α1 = α2 = ... = p), then the final result of interest rate using average daily balance would be the same with the daily balance interest rate system. According to Devie (2000) and Goenawan (2000), almost all conventional banks implement daily-based interest rate as each marketing strategy of conventional banks do not merely depend upon their interest rate calculation, but on points granted to customers or level of their annual interest rate.

2.3. Metric Interest Rate System

The metric interest rate system is calculated thoroughly based on second-based time order, for instance one second or metric interest rate per second, or even smaller for instance, half second, one tenth second, until zero second (Goenawan, 2013). The metric interest rate system, which the calculation using near zero range of period, is called continuous or real time metric interest rate system. Before presenting the formula of average-daily-based balance interest rate system and daily-based balance interest rate system, Table 2 below presents credit and debt within second-based range of time.

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��

��

Note:

p = transaction index of the time limit where interest rate final calculation of the savings is printed (printout). In general, the value of p would end up within one month (29, 30, or 31 days).

i = variable of days with range of days started from zero (0) until p.

m = the accumulated seconds from ∆di to ∆dp. In general, for one month, m = 29 × 86.400 seconds, m = 30 × 86.400 seconds, or m = 31 × 86.400 seconds.

Di = size of the accumulation of final credit (+) or debt (-) at the period (day) i.


∆di = range of periods (days) between di to di+1. Details of time measurement di, di+1 and ∆di approach zero second or real time.

With details of time measurement di, di+1 and ∆di will be.

1. One second is called metric average interest rate of 0 or metric average.

2. Half second is called metric average interest rate of 0.3.

3. One tenth second is called metric average interest rate of 1.

4. One hundredth second is called metric average interest rate of 2.

5. 1/10n second is called metric average interest rate of n.

Moreover the important feature of Metric Average Interest Rate System of n or Metric Interest Rate System of n (0 ≤ n < ∞) have the same result in terms of interest rate calculation. As the results of the interest rate calculation are actually the same, then the classification is determined to be Metric Average Interest Rate System (2.4.1) and Metric Interest Rate System (2.4.2). Meanwhile the classification will be Continuous Metric Interest Rate System if n is closed to infinity (∞).

The Calculation of Metric Average Interest Rate System

According to Goenawan (2013), the calculation of metric average interest rate system needs to be preceeded by the average balance ever per second within particular period, which is mostly one month (SRM). The formula of metric average interest rate can be expressed as follow.

m = ∆d1 + ∆d2 + .... + ∆dp (5)

SRM = (D0 × ∆d1 + (D0+D1) × ∆d2 + .... + (D0+D1+...+Dp-1) × ∆dp) / m (6)

BRM = SRM × m × β (7)

β is interest rate per second based on balance of SRM, which is known as annual interest rate divided by number of seconds within a year (1 year = 365 days, 1 day = 86400 seconds); SRM is metric average balance within m seconds; and BRM is metric average interest rate within m seconds.

The Calculation of Metric Interest Rate System

According to Goenawan (2013) the calculation of metric interest rate system is done within seconds during certain period which is commonly in 1 month. Before presenting the formula of metric interest rate, there are several additional points:


βi is interest rate per second based on balance at second i, which is commonly known as annual interest rate divided by the number of seconds within a year (1 year = 365 days and 1 day = 86400 seconds)

BSM is metric balance interest rate that is received within m seconds.

BSM = D0×∆d1×β1 + (D0+D1)×∆d2×β2 + .... + (D0+D1+...+Dp-1)×∆dp×βp (8)

It is important to note that if interest rate calucation does not involve size of balance or progressive interest rate (β = β1 = β2 = ... = βp) then the final result of metric average balance of interest rate system and metric balance of interest rate system would be the same.

The Calculation of Continuous Metric Interest Rate System

According to Goenawan (2013), the continuous metric interest rate system is actually the same with the compounded continous metric interest rate system as the interest rate calculation system can actually be done everytime. The result of continuous metric interest rate system is basically the compounded interest rate within seconds or real time. The calculation of continuous metric interest rate system will end up at period p which is particularly 1 month.

Moreover it can be defined as follow :

n is real number ranging 0 ≤ n < ∞, while in the case of continuous metric interest rate system, the value of n is approaching the infinity (∞).

p is the accumulation of seconds until the last count down where total calculation of the interest rate is printed (printout). The value of p is calculated with 1 month range of period expressed by p=29×86400 seconds, p=30×86400 seconds, or p=31×86400 seconds.

is the continuous metric interest rate factor, where d is current seconds (real time) and α is the annual interest rate system.

i is variable of seconds ranging from zero (0) to p.

j is variable of seconds ranging from one (1) to p.

Di is size of credit (+) and debt (-) at secind i.

∆di is period of seconds ranging from Di to Di+1.

Zj is balance and interest rate ranging from second j–1 until j.

∆Zj is interest rate balance ranging from second j–1 to j.

∆Zt is final interest rate balance at second p.

The principle is D0 + D1 + ... + Di .


��

��

��

��

(9)

(10)

The General Equation:

So that the total interest rate at the of period p is ∆Zt

∆Zt = ∆Z1 + ∆Z2 + … + ∆Zp or (11)

2.4. Vulnerability of Interest Rate System of Conventional Banks

Fundamental basis of the emergence of vulnerability of the system comes up from the simple fact that one day is equal to twenty four hours or is equal to 86400 seconds. The next fundamental theory expresses that daily interest rate system uses changes of date approach of account to calculate in the interest rate (Devie, 2000), thus in the case of changes of date, the customers have the right to claim the interest rate even though it can actually claimed after certain period, particularly once in a month, based on the policy of the banks. In order to better capture the sense of the theory, it is illustrated by the two simple cases.

2.4.1 The First Case

In the first case it is illustrated that how customers of Bank A multiply their interest rate within a month. Bank A implements daily interest rate system.

Supposed that Candra living in Jakarta is the customer of Bank A. Candra has friends who live in Bali namely Budi and the other one in Papua namely Ali. Both of his friends are the


customers of Bank A, They would work together by providing contribution amounted to 15 million Rupiah for each, so that the total fund would be 3 15 million Rupiah = 45 million Rupiah.

The scenario is that Candra would transfer 15 million to Ali’s account by 1 June 21.00 WIB. At the same time, Budi would also transfer 15 million to Ali’s account at 22.00 WITA. Using the real time transfer system, then both of those transfers would be recorded to be recevievd by Ali in 1 June at 23.00 WIT.

Later in 2 June 00.30 WIT, Ali would transfer the 45 million Rupiah back to Budi. The fund will be recorded in Budi’s account in 1 June at 23.30 WITA. Subsequently in 2 June at 00.30 WITA, Budi would transfer the 45 million to Chandra where it would be recorded to be received by Candra in 1 June at 23.30 WIB.

On the next day, which is 2 June 21.00 WIB, Candra would transfer 45 million Rupiah to Ali’s account. It would be recorded in Ali’s account in 2 June at 23.00 WIT. The process would keep working as mentioned above that Ali would transfer the 45 milion to Budi’s account in 3 June at 00.30 WIT, and in 3 June 00.30 WITA, Budi would transfer the 45 million back to Candra.

If the scenario runs simultaneously and real time, then those three people will earn interest rate three times higher than the nominal interest rate of Bank A.

Figure 3. Illustration of fund transfer by 3 people in 3 different time horizon of regions

2.4.2 The Second Case

In this case, it is illustrated how customers of international conventional bank K multiply their interest rate five times higher than the nominal interest rate, they could have earned in a


month. Supposed there are five friends (A, B, C, D, and E) who have savings in conventional bank K. A lives inColorado (UTC-7), B lives in New York (UTC-5), C lives in Jakarta (UTC+7), D lives in Bali (UTC+8), and E lives in Darwin (UTC+9.5). Supposed that deposit rate in US is 0.75%, Indonesia is 1%, and Australia is 0.5%. They agree to contribute USD 2,000 for each assuming daily interest rate takes place.

The scenario illustrates how A, B, C, D, and E optimize their interest rate up to five times higher during one month. Supposed that Colorado is 07.00 UTC-7, then in New York it would be 09.00 UTC-5, while in Jakarta it is 14 hours ahead from Colorado, which is 21.00 UTC+7, Bali is 22.00 UTC+8, and Darwin is 23.30 UTC+9.5. The total fund is US$ 10,000.

The scneario starts with initial transfer amounted to US$ 2,000 from A to E’s account on 1 June 07.00 UTC-7 and so do B, C, and D who would respectively transfer US$ 2,000 to E’s account on 1 June 09.000 UTC-5, 21.00 UTC+7, and 22.00 UTC+8. Assuming it is real time transfer, the money would be received by E on 1 June 23.30 UTC+9.5.

Then on 2 June at 00.30 UTC+9.5, E would transfer the US$ 10,000 to D’s account. This money would be recorded to be received by D on 1 June 23.00 UTC+8. Subsequently on 2 June at 00.30 UTC+8, D would transfer the US$ 10,000 to C’s account in which the transaction will be recorded on 1 June at 23.30 UTC+7. The next turn is that C transfers the US$ 10,000 to B’s account on 2 June at 00.30 UTC+7 in which the transaction will be recorded to be received by B on 1 June 12.30 UTC-5. And the last is that on 2 June at 00.30 UTC-5, B would transfer the US$ 10,000 to A’s account, which will be recorced to be received by A on 1 June 22.30 UTC-7. And their job is done on the transfer between 1 and 2 June.

Figure 4. Illustration of transfer among 5 parties in 5 different time-horizons


The process would run repeatedly in the next days which is the transfer between 2 and 3 June all the way to the next days in one month. If the scenario is simultaneously running in one month then each of them would earn five times higher interest rate based on the following calculation.

If A, B, C, D, and E do not cooperate, then each of them would only receive nominal interest rate granted by Bank K. Interest income earned by A and B is US$ 1.25, for C and D is US$ 1.6667, and for E is US$ 0.8333. The result is obtained from the multiplication of monthly interest rate ((0,75/12)%, (1/12)%, with (0,5/12)%) with the respective money by US$ 20,000 saved in one month.

Meanwhile if A, B, C, D, and E cooperate then each of them would receive five times higher interest income compared to nominal interest they could have earned granted by Bank K. Interest income for A and B is US$ 6.25, for C and D is US$ 8.3333, and for E is US$ 4.1667. This result is obtained from the multiplication between monthly income ((0,75/12)%, (1/12)%, dan (0,5/12)%) with their money amounted to US$ 10,000 saved in one month.

III. METHODOLOGY

The research employs comparative study to perform the metric interest rate system proposed by this research. To support the assumption, this paper gathers some information through questionnaires to customer service division of BCA, BRI, BNI, BMRI, and CIMB to figure out the process of real practice of online financial transfer.

There are ten questions to ask:

1. Is the interest rate calculated on daily basis? Yes

2. Does daily interest rate mean it will be granted to customers after they save their money in one day? Yes

3. Is it one day savings if there is change of date in the account? Yes

4. Is the money transfer available online? Yes

5. Is transfer via the same ATM provider conducted online? Yes

6. Can transfer via different ATM provider be conducted online? No

7. Is it online financial transfer if the transfer is done real time (even if there is differences, it would be just seconds)? Yes

8. Does the report sent by sender (Bandung) and received by beneficiary (Jakarta) have the same time range (even if there is time differences, it would be just seconds)? Yes

9. Does the report sent by sender (Papua) and received by beneficiary (Jakarta) have the same time range? Yes, the same with the local time.


10. Is transfer via the same ATM provider free from charge? Yes, no charge (depends on the bank)

In order to test the validity, it is calculated using the real account of the conventional banks. The result confirms that the calculation of interest rate is on daily basis.

The research method also tests whether daily interest rate system is customers’ savings within a day in which the customers could claim the interest after one day. Period of the tests consists of 30 days (1 month) in which the first test is initiated by adding the balance after 21.00, and then withdraw it back not long after a change of the date. Moreover the second test is done by transferring the fund in the middle of the night and in the evening right before date change occurs at the moment the money is withdrawn. At the end of the month, the account will be printed out to verify and analyze interest income from balance transaction of the savings during one month.

The observations reveals that one-day balance is not precisely 24-hours money saved in the bank. This research will discover what daily interest rate really means. In fact it benchmarks to the amount of final balance at the moment the date has changed, thus even money that has been just saved for 4 hours would receive one day interest income. The calculation result of those interest rate systems is subsequently compared within seconds order, including metric average interest rate system, metric interest rate system, and continuous metric interest rate system.


4.1. Estimation Result and Interest Rate Comparison

In general, there are two calculation systems of interest rate of conventional banks which are interest rate system based on daily average balance and daily balance. In calculating the interest rate, conventional banks tend to set one year as 365 days. In order to provide better comprehensive understanding regarding metric interest rate system, then the experience of Mr. Toni is elaborated comprehensively below. Supposed that Mr. Toni opens an account in Bank A amounted to 1 million Rupiah on 2 January at 11:00:45. Detail information regarding the transaction during 1 month (31 days) is exhibited below

Date Time Transaction Nominal

02.01.15 11:00:45 Cash Deposit Rp. 20.000.000,-

03.01.15 10:00:45 Transfer of Credit Rp. 5.000.000,-

04.01.15 13:30:50 Transfer Payment Rp. 10.000.000,-

20.01.15 12:30:55 Transfer Withdrawal Rp. 10.000.000,-


Supposed that the final calculation of daily interest rate will be in 31.01.2015 at 24:00:00. The annual interest rate is 8%, thus the daily interest rate will be α = 8% ÷ 365. The above example of financial transaction can be restructured to identify how long the balance has been settled with days or seconds in the bank.

Date Balance Σ days settled Σ seconds settled

02.01.15 Rp. 20.000.000,- 1 ( 3 – 2 ) 82.800

03.01.15 Rp. 25.000.000,- 1 ( 4 – 3 ) 99.005

04.01.15 Rp. 35.00.000,- 16 ( 20 – 4 ) 1.378.805

20.01.15 Rp. 25.000.000,- 11 ( 31 – 20) 991.745

Daily Average Interest Rate

Daily average interest rate performs the calculation of one-month interest rate using average balance of the current month. The calculation of average balance is based on the final daily amount of balance within the current one month divided by the number of days within the month or the number of days where balance has been settled for one month for those who initially open the account. Using equation (1) – (3), then it is calculated the daily average balance as follow.

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��

��

��

The average balance of January (31 – 2 = 29 days)

r = ∆h1 + ∆h2 + .... + ∆hp = 1+1+16+11 = 29 (12)

SRH = (H0 × ∆h1 + (H0+H1) × ∆h2 + .... + (H0+H1+...+Hp-1) × ∆hp) / r

= {(2·107 × 1) + (2,5·107 × 1) + (3,5·107 × 16) + (2,5·107 × 11)} / 29

= Rp. 30.344.827,586 (13)


BRH = SRH × r × α

= 30.344.827,586 × 29 × (0.08/365)

= Rp. 192.876,712 (14)

Daily Interest Rate System

The calculation of interest rate generated from daily balance is the basic aspect of daily interest rate system. The daily balance system of the current month is calculated by summing up the result of daily interest rate calculation for the whole one month. BSH is daily balance interest rate received by customers within the period of p. BSH is obtained using equation 4.

BSH = H0×∆h1×α1 + (H0+H1)×∆h2×α2 + .... + (H0+H1+...+Hp-1)×∆hp×αp (15)

As it is not progressive interest rate α = α1 = α2 = ... = αp = 0.08/365, then

BSH = {H0×∆h1 + (H0+H1)×∆h2 + .... + (H0+H1+...+Hp-1)×∆hp} × α (16)

The calculation of daily interest rate

BSH = {(2·107×1) + (2,5·107×1) + (3,5·107×16) + (2,5·107×11)}×(0.08/365)

= Rp. 192.876,712

As it is not progressive interest rate (α = α1 = α2 = ... = αp), then the final result of daily average interest rate system and daily interest rate will be the same.

Metric Interest Rate System

Besides the date it self, the primary characteristics of metric average interest rate is that every single transaction must be equipped with data of hours, minutes, and seconds. The transction of Mr. Toni during January can be expressed using second-based period as exhibited in Table 5 below.

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In order to figure out the Metric Average Balance (SRM) then the average balance must be calculated in the first place using second-based period divided by the number of seconds within m seconds period. SRM is obtained using equation (5) – (7).

1 year = 365 × 24 × 60 × 60 = 31.536.000 seconds.

m = ∆d1 + ∆d2 + .... + ∆dp

= 82.800 + 99.005 + 1.378.805 + 991.745 = 2.552.355 (18)

SRM = (D0 × ∆d1 + (D0+D1) × ∆d2 + .... + (D0+D1+...+Dp-1) × ∆dp) / m

= {(2·107 × 82.800)+(2,5·107 × 99.005)+(3,5·107 × 1.378.805)+

(2,5·107 × 991.745)} / 2.552.355

= Rp. 30.239.886,301 (19)

β is interest rate per second, β = 8% ÷ 31.536.000.

BRM = SRM × m × β

= 30.239.886,301 × 2.552.355 × (0.08/31.536.000)

= Rp. 195.796,359 (20)

Compared to the calculation of interest rate using daily average interest rate system (Rp.192,876.712), the result is apparently different with the result using metric average interest rate system (Rp.195.796,359).

Metric Interest Rate System

The primary characteristics of data required by the calculation of metric interest rate system is basically the same with the metric average interest rate system in which besides the date itself, the data of hours and seconds-based period are also required. The result of metric interest rate system is obtained from the multiplication with balance within second-based period. The calculation of metric interest rate system is done by summing up the result of daily interest rate calculation within one month. As exhibited by Table 5, transaction of Mr. Toni during January is in the form of metric interest rate system. BSM is metric balance interest rate received by customers within m seconds. BSM can be obtained using equation (8).

BSM = D0×∆d1×β1 + (D0+D1)×∆d2×β2 + .... + (D0+D1+...+Dp-1)×∆dp×βi (21)


As it is not progrssive interest rate β = β 1 = β 2 = ... = β p = 0.08/31.536.000, then

BSM = {D0×∆d1 + (D0+D1)×∆d2 + .... + (D0+D1+...+Dp-1)×∆dp}×β (22)

The calculation of metric balance interest rate

BSM = {(2·107 × 82.800)+(2,5·107 × 99.005)+(3,5·107 × 1.378.805)+

(2,5·107 × 991.745)} × (0.08/31.536.000)

= Rp. 195.796,359 23)

As it is not progressive interest rate (β = β1 = β2 = ... = βp), then the final result of the calculation between metric average interest rate system and metric interest rate system will be the same.

Continous Metric Interest Rate System

The output of continuous metric interest rate system is obtained from the ongoing calculation of balance. The calculation of the interest rate can be done by summing up the result of interest rate calculation continuously until the end of the month. The calculation of continuous metric balance interest rate is obtained using equation (9) – (11)

��

��

� ��

��

��

��

��

: Continuous Metric Interest Rate Factor,

Where variable d is current seconds and β is interest rate per second.

Z1 = D0 × G∞(∆d1) = 2·107 × G∞(82.800) = 2·107 × exp{[0,08/(365×86400)]×82800}

= 20.004.201,354

∆Z1 = Z1 – H0 = 20.004.201,354 – 20.000.00 = 4.201,354

Z2 = (D0 + D1) × G∞(∆d2) = (2·107+0,5·107) × G∞(99.005)

= 2,5·107 × exp{[0,08/(365×86400)]×99.005}

= 25.006.279,644


∆Z2 = Z2 – (D0 + D1) = 25.006.279,644 – 25.000.000 = 6.279,644

Z3 = (D0+D1+D2) × G∞(∆d3) = (2·107+0,5·107+1·107) × G∞(1.378.805)

= 3,5·107 × exp{[0,08/(365×86400)]×1.378.805}

= 35.122.634,882

∆Z3 = Z3 – (D0+D1+D2) = 35.122.634,882 – 35.000.000 = 122.634,882

Z4 = (D0+D1+D2+D3) × G∞(∆d4) = (2·107+0,5·107+1·107 –1·107) × G∞(991.745)

= 2,5·107 × exp{[0,08/(365×86400)]×991.745}

= 25.062.975,240

∆Z4 = Z4 – (D0+D1+D2+D3) = 25.062.975,240 – 25.000.000 = 62.975,240 (24)

Thus the continuous metric balance interest rate is ∆Zt :

∆Zt = ∆Z1 + ∆Z2 + ∆Z3 + ∆Z4

= 4.201,354 + 6.279,644 + 122.634,882 + 62.975,240

= Rp. 196.091,120 (25)

Comparing the above two calculations, then continuous interest rate system would provice higher interest rate. The result of the daily average interest rate system is equal to daily interest rate system amounted to Rp. 192,876.7. The result of metric average interest rate system is equal to the metric daily interest rate system amounted to Rp. 195,796.3, while continuous metric interest rate system is amounted to Rp. 196,091.1.

4.2. Solution with Metric Interest Rate System

Solution with metric interest rate system is used as a benchmark of the vulnerability of the above daily interest rate system. The output of this research tries to measurably discover that metric interest rate system is fair to the customers of conventional banks as well as mitigating potential risk the banks may face in the future. Beside date, the main characteristics of data required by the calculation of metric interest rate system is hours, minutes, and seconds-based period in the book account.

The result of calculation using daily average interest rate system is amounted to Rp.295,890.41, while the metric interest rate system is Rp.259,315.07. Those two calculation systems apparently provide different result where the average daily interest rate system provides higher result compared to metric average interest rate system.


Referring to the first case, the result of metric average interest rate system between Candra and Ali are quite different. Candra earned Rp.259.315,07 which is higher than interest received by Ali amounted to Rp.21,438.36. Such differences would reduce potential cooperation in terms of transfer between the two people. Such different result would occur if conventional banks keep using daily interest rate system for its real time financial transaction facilities.

The problem solving for these vulnerabilities on daily interest rate system can be broken dow by the following steps to help conventional banks avoid potential losses.

1. Driving transfer fee between similar or different banks higher. It is actually not strategic comprehensive solution as it does not a good promotion for the bank. Furthermore, banks’ profit may decrease due to declining deposits from customers.

2. Interbank financial transfer system is restricted to not make it real time for particular hours, commonly known as cut-off for particular hours, for instance cut-off started from 24.00 until 01.00, in which the system is not real time, still, it is delay for 1 hour. This solution still have weaknesses as 1-hour restriction would not be feasible for regions with 2-hour time lag. If the time restriction is improved to be two hours, it would not completely solve the problem as the restriction would not be feasible for regions with 3-hour time lag. The restriction policy would only run effectively if it takes 24 hours. It is absolutely not comprehensive in the era of RTOL information technology.

3. Time duration of transfer needs to be outright among customers even though they are living in different regions. It is not a comprehensive solution as it may get the first transferee confused. For instance Adi, who lives in Jakarta, transfers his money using RTOL to Budi who lives in Papua. Then the date that would printed out on Budi’s account is 08.00 WIT, in fact it is 10.00 WIT.

4. The timing of granting the interest rate has to be outright even though the customers live in different regions. Supposed that a national bank implements this policy (granting interest rate simultaneously) and refer to Western Indonesia Time (WIB). It would automatically calculate the daily interest rate of all final amount of balance of customers of all branches throughout Indonesia at 24.000 WIB. As the consequences branches of the national banks located in Central Indonesia Time (WITA) and Eastern Indonesia Time (WIT) would start the calculation of the interest rates at respectively 01.00 WITA and 02.00 WIT. This solution is clearly incomprehensive as any balances of different date/day would not receive interest rate while others living in a region the time horizon is being referred to get the interest rate. For instance Adi, who lives in Bali, transfers 10 million Rupiah at 00.30 WITA. After one hour at 01.30 WITA of the same day, the money is subsequently transferred to his business partner. Referring to the calculation system, Adi would earn interest rate from the 10 million Rupiah. In the other side Budi, who lives in Papua and gets the transfer amounted to 10 million Rupiah at 02.30 WIT. After 23 hours later, which is 01.30 WIT on the same day,


the money is transferred to his other business partner. As the consequences, Budi would not earn interest rate from the 10 million.

The above incomprehensive solutions might be able to solve one problem, still, it tends to come up with the new problem. In order to avoid the new problems, then it is proposed more comprehensive solution. The comprehensive solutions is to implement metric interest rate system to cover the vulnerability of daily interest rate system. The strengths of metric interest rate system are explained as follow.

1. Through metric interest rate system, the calculation of the interest rate would be based on seconds order i.e. one second, thus the minimum time lag approaches zero second. The system is able to cover the problem of the emergence of multiple interest rate in the daily average interest rate system.

2. The use of metric interest rate system does not require transfer fee between conventional banks, both some and different providers. The policy would absolutely benefit the customers as they feel no doubt to use the transfer services provided by the banks.

3. By implementing the matric interest rate system then inter-bank financial transaction could remain real time in 24 hours a day. Thus it is no longer necessary to restrict or to provide cut-off in particular time, for instance between 21.00 and 24.00 the system would not be real time, which many of conventional banks still embrace. Banks with unreal time system would precisely face potential losses if they still embrace the daily interest rate system. It would happen if the customer withdraw their money by 5 million Rupiah via ATM at 22.05 WIB. As within the range of time the system postpone to record the real time transaction until the date/day changes, then the consequence would be the customer has the right to claim the one-day interest rate of the 5 million balance he had withdrawn earlier.

4. The use of metric interest rate system make the calculation of daily interest rate no need to be outright even though the banks’ branches are located in different time horizon.

The problem of interest rate vulnerability would emerge as rapid development of information and communication technology leading to real time financial transaction, even though customers are located in different time horizons. Therefore it requires comprehensive changes in banking interest rate system in the 21st century. If those banks are not willing to change comprehensively, then there would be two possible things to happen. The first one is that the use of daily interest rate would drive customers, both individual and corporates, to benefit the vulnerability that could create potential losses for the conventional banks. The possibility is that conventional banks with daily interest rate system would be leaved by its customers if metric interest rate system is implemented by its competitors.

There are number of analysis suggesting that the use of metric interest rate system would not burden conventional banks with potential losses. Those aspects are:


1. Metric interest rate system could avoid unfair interest rate for customers regarding claim on daily interest rate that does not yet reach the whole one-day.

2. Metric interest rate system could avoid multiple interest rate due to vulnerability of daily interest rate system based on changes in date.

3. The difference of the interest rate between daily interest rate and metric interest rate is not high. For instance, as stated in the sub-chapter 5.1, metric system tends to provide higher interest rate compared to daily interest rate system by 0.017. The positive side is that the difference between interest rates coming from the two systems is relatively small, but the psychological effect is quite large. Psychologically the customers tends to choose metric interest rate system compared to daily interest rate system.

It assumes that credit distribution before and after the implementation of metric interest rate system is the same, it terms of its smoothness. There is assumption that the metric interest rate system would benefit conventional banks. It could happen as the metric interest rate system is more psychologically interesting compared to daily interest rate. This would attract more customers to save money and conduct transaction in the conventional banks.

4.3. Obstacles in Implementing Comprehensive Solutions

Metric interest rate system has never been used by any conventional banks in Indonesia, even by banking industry in the world. As the consequences many countries refer to banking interest rate system from abroad, even tend to be passive. Other aspects that could hamper banking sector of Indonesia to implement metric interest rate system is the Letter of Bank Indonesia No. 11/35/DPNP on The Report of New Products or Activities, that has effectively run since 31/12/2009.

The existing mechanism within the regulation shows the urgency to get the approval from Bank Indonesia on the new product from banking sectors, and it could take longer time to take place. We suggest any adaptive actions and strategic policy that could eliminate the vulnerability of the daily interest rate system. Modern banking industry should also anticipate the vulnerability of this system due to rapid development of information and communication technology. Nevertheless according to the Letter of Bank Indonesia 11/35/DPNP on The Report of New Products or Activities that has effectively run since 31/12/2009, then banking sector cannot necessarily come up with comprehensive solution in the form of new product as it requires approval from Bank Indonesia.

4.4. Opportunities of the National Authority and Banking

Indonesia, which is represented by Bank Indonesia, could become pioneer to provide new policy for the sake of public worldwide. The new regulation could be the implementation of metric interest rate system as solution to prevent any vulnerabilities driven by daily interest rate


system. The vulnerability of daily interest rate system emerge due to globalization and rapid development of information and communication technology. In the global era, banks could provide real time financial transaction to all customers throughout Indonesia, ASEAN, and even the globe with the respective time horizons. The consequences of the implementation of metric interest rate system, banking sector could provide better service to its customers and ends up with high competitiveness in this global era.

Indonesia has been participating in ASEAN Economic Community (AEC) since 2016. This moment should be well-utilized by Indonesia, represented by the government, to promote metric interest rate system. The benefit for the government could be in the form of tax revenue from the license and royalty from the new system. The user of this new system could be any banks throughout Indonesia, ASEAN, even the globe.

In terms of the interest of the national banking sector, the use of metric interest rate system is a comprehensive solution with the number of benefits both banks and customers could perceive.

1. The conventional banks could establish branch offices in across location with different time zones without establishing cut-off policy of financial transaction in the middle of the night, just like what conventional banks are currently running with.

2. The implementation of metric interest rate system is a promotion tool for modern banking as it psychologically attracts more customers to deposit their money in the banks.

3. With metric interest rate system, customers whose less-than-24 hours deposit would not be harmed.

4. The use of metric interest rate system is a “win-win solution” strategy as banks would not be harmed. The principle of justice would be more measureable. If conventional banks still implement daily interest rate system, the bank could just grant the daily interest rate to the customers whereas the deposit does not take the whole one day, even just couple hours.

5. From the customer perspective, the national banking sector that develop its branches trans-nationally would experience higher competitiveness in terms of more attracting interest rate and the ability to provide RTOL service within 24 hours.

In order to make sure that all provided information to customers regarding RTOL is valid, and the improvement of banking quality in providing service of online financial transaction, it is strongly suggested that Indonesia establishes Rating Center for Banking RTOL Transaction System. In order to supervise the vulnerability of the daily interest rate system, banking sector could implement cut-off policy for particular period of time. The policy may harm customers, thus in order to avoid this side effect, then the government (through Bank Indonesia) or private sectors need to establish rating center for banking RTOL transaction system.


The rating center would be the first in Indonesia, even probably in the world. The goal of establishing this center is to provide accurate and objective information to customers and banking sector, mainly to provide feedback in the case of potential vulnerability come up from RTOL transaction as well as potential risk of losses perceived by banks and customers due to daily interest rate system. The other goal is to become component of banking marketing strategy to attract higher deposit from customers through improvement of services on RTOL financial transaction.

V. CONCLUSION

In the 21st century, the National/International Banking sector needs comprehensive strategic solution to face any vulnerabilities due to daily interest rate system. Banking sector is strongly suggested to be in line the global dynamic where financial transaction is connected real time due to rapid progress of information and communication technology. If it takes longer to decide, then it would harm the firm in the near future.

Referring to the above mentioned two facts, financial transaction can be done with RTOL while national/international banking sector that have branch offices located in different time zones could possibly be harmed by the fundamental vulnerability of daily interest rate system.

The vulnerability of daily interest rate system could be solved using both incomprehensive and comprehensive alternatives. The incomprehensive solutions include higher transfer fee, unreal time transfer system, outright time recording system, or the daily interest rate is calculated simultaneously. Eventhough those four incomprehensive solutions could overcome the vulnerability of the daily interest rate system, still, it would create other potential new problems.

The comprehensive solution using metric interest rate system provides the process of interest rate calculation using second-order of time, even almost zero second. Besides the date, the main characteristics of metric interest rate system is that it also requires more detail data such as hours, minutes, even seconds. The comprehensive solution would benefit both customers and banks in terms of profit and efficiency.

The above illustration performs the strength of metric interest rate system in which conventional banks could overcome the vulnerability driven by daily interest rate system due to rapid improvement of information and communication technology that enables real time financial transfer throughout the world.

Regarding the vulnerability of banking interest rate system, the government of Indonesia could become the pioneer of metric interest rate system. The government of Indonesia could also benefit the policy within the national and international scopes in the form of tax revenue upon the use the metric interest rate system.


REFERENCES

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