T.C.

ISTANBUL UNIVERSITY

INSTITUTE OF BUSINESS ADMINISTRATION

MASTER OF BUSINESS ADMINISTRATION

MBA GRADUATE PROGRAM

PREDICTION OF ECONOMICAL RECESSION WITH THE SIGNAL

APPROACH, CASE: TURKEY

Project Instructor : Prof. Dr. Mehmet Şükrü TEKBAŞ

Prepared by Deniz Özgür Tiryaki, 9501112729

TERM PROJECT

JANUARY 2013

İşletme Fakültesi İşletme İktisadı Enstitüsü ............................. no'lu öğrencilerinden

..................................................................'in bitirme projesi olarak yaptığı

"........................................................................................" başlıklı çalışması,

....................... tarihinde, değerlendirilerek başarılı/başarısız bulunmuştur.

Bitirme Projesi Danışmanı

iii

ABSTRACT

Although prediction of financial crisis is a hard subject, most of economists are

interested in this subject. The aim of this project is determining a model for

predicting of recessions in economy for the 2008 Turkey crisis. In this article, it is

used signal approach for providing a signal before September 2008, starting of

recession. It is important to understand the economical behavior of indicators before

the crisis times, and most of economists are interested in the selecting of the most

trustful indicators. This article uses nine different indicators for predicting a possible

crisis. It is important to understand and evaluate each financial crisis, and determine

a model for 2008 Turkey crisis. In addition to this, I hope it can be useful for

determining future recessions in economy.

iv

PREFACE

First, I want to thank my instructor Prof. Dr. Mehmet Şükrü TEKBAŞ for the

contribution in this project. Second, the great support of my family, my father and

mother, in the all of my academic life, Third, special thanks to my dear friend Barış

Öz.

January, 2013 Deniz Özgür Tiryaki

v

TABLE OF CONTESTS

Page

ABSTRACT................................................................... ............................................. iii

PREFACE............................................................ ....................................................... iv

TABLE OF CONTESTS................................. ............................................................ v

LIST OF TABLES........................................... .......................................................... vii

LIST OF FIGURES.......................................... .......................................................... ix

1. INTRODUCTION............................................ ....................................................... 1

1.1 Importance of the Issue .............................................................................. 1

1.2 The Methodology ....................................................................................... 2

2. DEFINITION OF FINANCIAL CRISIS AND THE TYPES OF FINANCIAL

CRISIS................................................................. ........................................................ 4

2.1 Financial Crisis .......................................................................................... 4

2.2 Types of Financial Crisis ........................................................................... 7

2.2.1 Historical Roots .................................................................................. 7

2.2.2 Important Crisis in 1990-2010 ........................................................... 8

3. FINANCIAL CRISIS DEFINITION MODELS ................................................... 12

3.1 Financial Crisis Models ........................................................................... 12

3.2 Financial Forecasting ............................................................................... 14

4. APPLYING THE SIGNAL APPROACH TO THE 2008 CRISIS IN TURKEY. 17

4.1 Signal Approach ...................................................................................... 17

4.2 Selection of the Macro Economical Indicators ........................................ 20

4.2.1 Golden Prices ................................................................................... 20

4.2.2 Employment Ratio ............................................................................ 22

vi

4.2.3 Currency Reserves ............................................................................ 24

4.2.4 Automotive Production .................................................................... 26

4.2.5 Export ............................................................................................... 28

4.2.6 Import ............................................................................................... 30

4.2.7 Industry Production Index ................................................................ 32

4.2.8 Real Currency Rate .......................................................................... 34

4.2.9 Unemployment ................................................................................. 36

5. COMBINED CRISIS INDEX.......................... ..................................................... 38

5.1 Combined Crisis Index with Same Weights ............................................ 38

5.2 Recalculating Combined Crisis Index ..................................................... 42

6. RESULTS...................................................................... ........................................ 44

ATTACHMENT-A.....................................................................................................49

ATTACHMENT-B.....................................................................................................50

7. REFERENCES........................................................................................................51

vii

LIST OF TABLES

Page

Table 1- Selected macro economical indicators ................................................... 18

Table 2 – Golden Price Analysis .......................................................................... 21

Table 3 – Golden Prices True Signal Ratio .......................................................... 22

Table 4 – Employement Ratio Analysis............................................................... 23

Table 5 - Employement True Signal Ratio .......................................................... 24

Table 6 – Currency Reserves Analysis ................................................................ 25

Table 7 – Currency Reserves True Signal Ratio .................................................. 26

Table 8 – Automotive Production Analysis ......................................................... 27

Table 9 – Automotive Production True Signal Ratio ........................................... 28

Table 10 – Export Analysis .................................................................................. 29

Table 11 – Export True Signal Ratio ................................................................... 30

Table 12 – Import Analysis .................................................................................. 31

Table 13 – Import True Signal Ratio ................................................................... 32

Table 14 – Industry Production Analysis ............................................................. 33

Table 15 – Industry Production True Signal Ratio .............................................. 34

Table 16 – Real Currency Analysis ..................................................................... 35

Table 17 – Real Currency True Signal Ratio ....................................................... 36

Table 18 – Unemployment Change Analysis ....................................................... 37

Table 19 – Unemployment True Signal Ratio ..................................................... 38

Table 20 - CCI values of this between the periods January 2005-2012 ............... 39

Table 21 – Combined Crisis Index Analysis ........................................................ 40

Table 22 – Combined Crisis Index True Signal Ratio ......................................... 41

Table 23 - CCI values of this between the periods January 2005-2012 after

recalculating ............................................................................................................... 42

Table 24 – Combined Crisis Index Analysis After Recalculation ....................... 43

Table 25 – Combined Crisis Index True Signal Ratio After Recalculation ......... 44

Table 26 – Comparison of Nine Indicators and Combined Crisis Index ............. 45

Table 27 – The values of Combined Crisis Index in the year 2006 ..................... 46

viii

Table 28 - Combined Crisis Index Analysis After Recalculation for the Year

2012 ............................................................................................................................ 47

ix

LIST OF FIGURES

Page

Figure 1 - Real GDP growth rates for 2009 ........................................................... 1

Figure 2 – Flow Diagram ....................................................................................... 4

Figure 3 – Anatomy of a modern EFM financial crisis ......................................... 5

Figure 4 – The relation between financial markets and intermediaries ................. 6

Figure 5 - A cycle of traditional forecast process ................................................ 16

Figure 6 – Signal approach table .......................................................................... 17

Figure 7 – Combined Crisis Index Change .......................................................... 41

Figure 8 - Combined Crisis Index After Calculation ........................................... 45

1

1. INTRODUCTION

1.1 Importance of the Issue

Global Financial Crisis of 2008-2009 has been the worst since the Great

Depression in the 1930s, and it has huge effects over Turkey’s economy. Starting

from the last quarter of 2008, government regulations follow the crisis. The Turkish

Government and Central Bank of Turkey are expected to take immediate and

concrete actions that restore the market confidence. Most of economists and market

participants consider and investigate the reasons of this financial crisis. The early

most common and reliable signs such as reduced financial activities and increased

unemployment levels of the financial establishments are now considered and thought

to be reliable factors to predict these financial crises in advance. It is important to

work over and understand every financial crisis individually to avoid from them.

The following countries went into recession in the second quarter of 2008:

Greece, Estonia, Latvia, Ireland and New Zealand. The following countries went

into recession in the third quarter of 2008: Japan, Sweden, Hong Kong, Singapore,

Italy, Turkey and Germany. As a whole the fifteen nations in the European Union

that use the Euro went into recession in the third quarter, and the United Kingdom.

In addition, the European Union, the G7, and the OECD all experienced negative

growth in the third quarter. It can be accepted that it is started in September, 2008 in

Turkey. Below the World map showing real GDP growth rates for 2009.

Figure 1 - Real GDP growth rates for 2009

2

A financial forecast is an estimate of future financial outcomes for a company or

country (for futures and currency markets). Using historical internal accounting and

sales data, in addition to external market and economic indicators, a financial

forecast is an economist's best guess of what will happen to a company in financial

terms over a given time period which is usually one year.

The question in this article that it is possible to predict financial recession, and if

it is possible, what type of indicators could be taken?

There are lots of methods to predict in literature, and signal approach method is

chosen for a prediction model in this article.

1.2 The Methodology

The IMF uses two major early warning systems for their surveillance activities

named the Developing Countries Studies Division (DCSD) and the Kaminsky,

Lizondo and Reinhard (KLR) financial crisis early warning systems.

In this article, it is used KLR method with signal approach.

The first step in a signals approach is to define what the researcher understands

by a crisis. KLR define a crisis as a ―situation where an attack to the currency leads

to a sharp depreciation of the currency, a large decline of international reserves or a

combination of the two‖ (KLR 1998). Notice that this definition includes successful

as well as unsuccessful attacks. KLR (1998) construct an index of exchange market

pressure that they use as a measure of currency crisis. This indexes calculated as a

weighted average of the monthly percentage changes in the exchange rate, ∆e/e , and

the monthly percentage changes in reserves, ∆r/r, with weights chosen in such a way

that the two components of the index have equal sample volatilities. That is,

(1.1)

3

where is the σe standard deviation of the rate of change of the exchange rate and is

the σr standard deviation of the rate of change of reserves. A currency crisis is

defined to occur when this index exceeds its mean by more than three standard

deviations.1

It can be used by an applier, different variables or indicators. In this article it is

used 9 different variables to calculate combined crisis index. The performance of

each indicator was evaluated using the following matrix;

Crisis

(Within 24 months)

No Crisis

(Within 24 months)

Signal was issued A B

No signal was issued C D

In this matrix, A is the number of months in which the indicator issued a good

signal, B is the number of months in which the indicator issued a bad signal or

―noise‖, C is the number of months in which the indicator failed to issue a signal, and

D is the number of months in which the indicator refrained from issuing a signal. A

perfect indicator would only produce observations that belong to the north-west and

south-east cells of this matrix.

The noise-to-signal-ratio ([B=(B + D)]=[A=(A + C)]) of a good indicator is low,

and its probability of a crisis conditioned on the occurrence of the signal (A=(A +

C)) is high. 2

The following figure shows the path taken during the course of the thesis.

1 (Peng, Bajuna, t.y.: 11-12)

2 (Palmerin, 2012: 1)

4

Figure 2 – Flow Diagram

2. DEFINITION OF FINANCIAL CRISIS AND THE TYPES OF FINANCIAL

CRISIS

2.1 Financial Crisis

It is stated that financial crisis term is a situation in which the value of macro

economical variables or assets drop rapidly in a certain period. In generally, it can

be associated with a run on the banks. For example, investors sell off assets or

withdraw money from saving accounts due to believe of those assets will drop if they

remain at a financial institution.

On the other hand, a financial crisis can come as a result of assets being

overvalued, and can be affected by investor behavior. A rapid string of sell offs can

further result in lower asset prices or more savings withdrawals. If left unchecked,

the crisis can cause the economy to go into a recession or depression.

A typical financial crisis occurs as below,

5

Figure 3 – Anatomy of a modern EFM financial crisis

This relation is generally between market and intermediaries. Financial systems

are crucial to the allocation of resources in a modern economy. They channel

household savings to the corporate sector and allocate investment funds among

firms; they allow intertemporal smoothing of consumption by households and

expenditures by firms; and they enable households and firms to share risks. These

functions are common to the financial systems of most developed economies. Yet

the form of these financial systems varies widely. This relation can be shown at the

scheme below.

6

Figure 4 – The relation between financial markets and intermediaries

Financial crisis may be accompanied by some of the features, which are

highlighted below.

A demand for reserve money so intense that the demand could not be

satisfied for all parties simultaneously in the short run.

A liquidation of credit that has been builds up in a boom.

A condition in which borrowers who in other situations were able to borrow

without difficulty become unable to borrow on any terms-a credit crunch or

credit market collapse.

A forced sale of assets because liability structures are out of line with market-

determined asset values, causing further decline in asset values-the bursting

of a price bubble.

A sharp reduction in the value of banks’ assets resulting in the apparent or

real insolvency of many banks and accompanied by some bank collapses and

possibly some run.

7

All of the elements emphasized above could be present in a financial crisis and

some may be more important than the other in a given situation of the crisis.3

2.2 Types of Financial Crisis

2.2.1 Historical Roots

First of all, it is provided a brief narrative of events which could be characterized

as global financial crises. It is also demarcated several episodes which were

primarily regional rather than global crises.

The essence of a financial crisis is a banking crisis. According to sources for an

episode to qualify as a banking crisis, it must be observed bank runs, widespread

bank failures and the suspension of deposits into currency such that the latter

circulates at a premium relative to deposits. This term also can be called banking

panic. Another reason is significant banking sector problems resulting in the erosion

of most or all of banking system collateral that are resolved by a fiscally

underwritten bank restructuring. This definition allows us to distinguish between pre

1914 banking panics in which lender of last resort intervention was either absent or

unsuccessful, and subsequent crises in which a lender of last resort or deposit

insurance was in place and the main problem was bank insolvency rather than

illiquidity.

Financial crises are aggravated when they lead to or are accompanied by currency

crises. This term is called as a speculative attack on a pegged exchange rate and debt

crises. Some of international financial crises are banking crises that are often

accompanied by currency crises.4 We provide a brief narrative of events which

could be characterized as global financial crises. We also demarcate several episodes

which were primarily regional rather than global crises.

When a bank suffers a sudden rush of withdrawals by depositors, this term called

as bank run. Since banks lend out most of the cash they receive in deposits, for

3 (Singh, t.y.: 6)

4 (Bordo, Lane, t.y.:3)

8

example fractional-reserve banking, it is difficult for them to quickly pay back all

deposits if these are suddenly demanded, so a run may leave the bank in bankruptcy,

causing many depositors to lose their savings unless they are covered by deposit

insurance. A situation in which bank runs are widespread is called a systematic

banking crisis as like as banking panic. A situation without widespread bank runs,

but in which banks are reluctant to lend, because they worry that they have

insufficient funds available, is often called a credit crunch. In this way, the banks

become an accelerator of a financial crisis. Examples of bank runs include the run

on the Bank of the United States in 1931 and the run on Northern Rock in 2007.5

The collapse of Bear Stearns in 2008 has also sometimes been called a bank run,

even though Bear Stearns was an investment bank rather than a commercial bank.

Banking crises generally occur after periods of risky lending and heightened loan

defaults. The U.S. savings and loan crisis of the 1980s led to a credit crunch which

is seen as a major factor in the U.S. recession of 1990–91.When a country that

maintains a fixed exchange rate is suddenly forced to devalue its currency because of

a speculative attack, this term is called as a currency crisis. Another name for this

term is balance of payment crisis. When a country fails to pay back its sovereign

debt, this is called a sovereign default. While devaluation and default could both be

voluntary decisions of the government, they are often perceived to be the involuntary

results of a change in investor sentiment that leads to a sudden stop in capital inflows

or a sudden increase in capital flight. Several currencies that formed part of the

European Exchange Rate Mechanism suffered crises in 1992–93 and were forced to

devalue or withdraw from the mechanism. Another round of currency crises took

place in Asia in 1997–98. Many Latin American countries defaulted on their debt in

the early 1980s. The 1998 Russian financial crisis resulted in a devaluation of the

ruble and default on Russian government bonds.

2.2.2 Important Crisis in 1990-2010

It is given some key notes about the important financial crisis in last 20 years.

5 (Bordo, Lane, t.y.:2)

9

The Japanese asset price bubble was an economic bubble in Japan from 1986 to

1991, in which real estate and stock prices were greatly inflated. The bubble's

subsequent collapse lasted for more than a decade with stock prices initially

bottoming in 2003, although they would descend even further amidst the global crisis

in 2008. The Japanese asset price bubble contributed to what some refer to as the

Lost Decade. Some economists, such as Paul Krugman, have argued that Japan fell

into a liquidity trap during these years.

Another important crisis is Sweden Banking Sector Crisis. Sweden has had an

economic model in the post-World War II era, characterized by close cooperation

between the government, labor unions and corporations. The Swedish economy has

extensive and universal social benefits funded by high taxes, close to 50% of GDP.

In the 1980s, a real estate and financial bubble formed, driven by a rapid increase in

lending. A restructuring of the tax system, in order to emphasize low inflation

combined with an international economic slowdown in the early 1990s, caused the

bubble to burst. Between 1990 and 1993 GDP went down by 5% and unemployment

skyrocketed, causing the worst economic crisis in Sweden since the 1930s.

According to an analysis by George Berglund published in Computer Sweden in

1992, the investment level decreased drastically for information technology and

computing equipment, except in the financial and banking sector, the part of the

industry that created the crisis. The investment levels for IT and computers were

restored as early as 1993. In 1992 there was a run on the currency, the central bank

briefly jacking up interest to 500% in an unsuccessful effort to defend the currency's

fixed exchange rate. Total employment fell by almost 10% during the crisis.

In politics and economics, Black Wednesday refers to the events of 16 September

1992 when the British Conservative government was forced to withdraw the pound

sterling from the European Exchange Rate Mechanism (ERM) after they were unable

to keep it above its agreed lower limit. George Soros, the most high profile of the

currency market investors, made over US$1 billion profit by short selling sterling. In

1997 the UK Treasury estimated the cost of Black Wednesday at £3.4 billion, with

the actual cost being £3.3 billion which was revealed in 2005 under the Freedom of

Information Act. The trading losses in August and September were estimated at

10

£800 million, but the main loss to taxpayers arose because the devaluation could

have made them a profit. The papers show that if the government had maintained

$24 billion foreign currency reserves and the pound had fallen by the same amount,

the UK would have made a £2.4 billion profit on sterling's devaluation. Newspapers

also revealed that the Treasury spent £27 billion of reserves in propping up the

pound.

The 1994 economic crisis in Mexico, widely known as the Mexican peso crisis or

the Tequila crisis, was caused by the sudden devaluation of the Mexican peso in

December 1994. The impact of the Mexican economic crisis on the Southern Cone

and Brazil was labeled the Tequila effect.

The Asian financial crisis was a period of financial crisis that gripped much of

Asia beginning in July 1997, and raised fears of a worldwide economic meltdown

due to financial contagion. The crisis started in Thailand with the financial collapse

of the Thai baht after the Thai government was forced to float the baht, cutting its

peg to the U.S. dollar, after exhaustive efforts to support it in the face of a severe

financial overextension that was in part real estate driven. At the time, Thailand had

acquired a burden of foreign debt that made the country effectively bankrupt even

before the collapse of its currency. As the crisis spread, most of Southeast Asia and

Japan saw slumping currencies, devalued stock markets and other asset prices, and a

precipitous rise in private debt.

The Russian financial crisis hit Russia on 17 August 1998. It resulted in the

Russian government devaluing the ruble and defaulting on its debt.

The early 2000s recession was a decline in economic activity which occurred

mainly in developed countries. The recession affected the European Union mostly

during 2000 and 2001 and the United States mostly in 2002 and 2003. The UK,

Canada and Australia avoided the recession for the most part, while Russia, a nation

that did not experience prosperity during the 1990s, began to recover. Japan's 1990s

recession continued. The early 2000s recession had been predicted by economists

for years, because the boom of the 1990s, which was accompanied by both low

inflation and low unemployment, had already ceased in East Asia during the 1997

11

Asian financial crisis. The early 2000s recession was not as bad as many predicted it

would be, nor was it as bad as either of the two previous worldwide recessions.

Some economists in the United States object to characterizing it as a recession, since

there were no two consecutive quarters of negative growth.

The 2008–2012 Icelandic financial crisis is a major economic and political crisis

in Iceland that involved the collapse of all three of the country's major commercial

banks following their difficulties in refinancing their short-term debt and a run on

deposits in the Netherlands and the United Kingdom. Relative to the size of its

economy, Iceland’s banking collapse is the largest suffered by any country in

economic history.

The financial crisis of 2007–2008, also known as the global financial crisis and

2008 financial crisis, is considered by many economists to be the worst financial

crisis since the Great Depression of the 1930s. It resulted in the threat of total

collapse of large financial institutions, the bailout of banks by national governments,

and downturns in stock markets around the world. In many areas, the housing

market also suffered, resulting in evictions, foreclosures and prolonged

unemployment. The crisis played a significant role in the failure of key businesses,

declines in consumer wealth estimated in trillions of US dollars, and a downturn in

economic activity leading to the 2008–2012 global recession and contributing to the

European sovereign-debt crisis. The active phase of the crisis, which manifested as a

liquidity crisis, can be dated from August 7, 2007 when BNP Paribas terminated

withdrawals from three hedge funds citing a complete evaporation of liquidity.6

6 (Sevim, 2012)

12

3. FINANCIAL CRISIS DEFINITION MODELS

In this part, it is defined shortly, financial crisis definition and prediction models.

3.1 Financial Crisis Models

The analysis of models initiated since 1979, with the first generation models

which explain the changeable financial crisis, as a predictable but inevitable event,

derived from the inconsistency of fiscal policy with the exchange and monetary rate,

that is to say the purpose of the monetary authority is to preset a policy of fixed

exchange rate, but simultaneously with the budget deficit.

The first generation models arise from imbalances in the public sector (balance of

payments) caused by speculative and decline in international reserves, with the work

of Paul Krugman (1979), based on the model from Kouri (1976) and the work of

Salant & Hederson (1978). However, the Krugman (1979) model is subject to two

important limitations, which he concludes: the model is based on a highly simplified

macro-economic model; this means that the analysis of the factors from the balance

of payments is incomplete. On the other hand, it is impossible to consider assuming

only two assets to reflect the reality. In more realistic models the exchange rate

would have to be stabilized with an open financial market. Flood & Garber (1984),

Connolly & Taylor (1984), Sachs (1986), Wijnbergen (1988) y Dooley (2000),

extend the work of Krugman (1979).

The synchrony between reality and the theoretic models from the first generation

made think that the unique cause for exchange crises was the public deficit.

However, the crises on the emerging countries have shown that the currency crises

are related to the stock market crises. García (2005), performs an analysis of several

empirical studies which rely on the first generation models, he says that these models

have a better explanation for the crises prior to the nineties.

The second generation models of financial crisis appear in the mid-80’s, where

expectations test the performance of the auto crisis, where a financial crisis may or

may not occur.

13

Obstfeld (1986), author of the Basic model from the second generation, focuses

on the relationship of expectations from the domestic agents on the decrease of the

exchange rate, considers devaluation as a decision of the governments, indicating

that the financial crisis may appear even if the fundamental variables are favorable

and there are no speculative assaults. Garber (1996), based on the Obstfeld (1986)

model and Eichengreen, Rose, & Wyplosz (1996) add the speculative assaults

identifying the foundations from the first generation models. Mishkin (1992),

assures that one of the reasons of the financial crises is precisely the asymmetric

information, being based on the fragility of the structure of debts that are used for

speculation.

The third generation models arise after the financial crisis of East Asia and join

the monetary crisis and the fragility of the financial sector and contagion from other

countries. Valdés (1997), explains how the necessity of liquidity for investors drives

to contagion effects. Kaminsky & Reinhart (1998), use contagion concepts: trade

relations and direct trade competition between countries or indirectly in a third

market. Eichengreen et al. (1996), Kaminsky & Reinhart (1998), demonstrate that

growth of private and public credit are indicators of currency crisis, and state that the

second generation models cannot be used to explain other financial crises where the

trade balance is not an indicator of monetary crisis, as well as the first generation

models.

Morris & Shin (1998) build a theoretical game model with asymmetric

information about fundamentals of the speculators. Calvo (1998) attributes

contagion to lack on financial market liquidity. Kodres & Pritsker (2002), argue that

countries with a high degree of mobility in their assets shown with the assets of

countries that are experiencing a financial crisis could be vulnerable to contagion via

market relations. Forbes & Rigobon (2002), analyze the stock market during 1997 in

Asia, 94 in Mexico and the 1987 crash of U.S. stock market, they define contagion

as a substantial increase on correlation between stock market during instability.

Corsetti et al. (1999), consider an optimization model of intertemporal

equilibrium of a situation of moral hazard (risk transfer), and is exercised by the

14

banking activity and firms operating under the presumption that they will be insured

against contingencies.

Luiz de Mello et al. (2001), considers how exchange rate movements affect

foreign debt portfolios, uses a dynamic panel model, the reason of exchange rate

movements depends not only on this one, but as well on external debt. The work of

Bakeart et. al (2002) and Lagunes & Watkins (2008) motivates Rodríguez, Cortez &

Torres (2008) to create an analysis on the contagion effects, for the case of México

1994 as a not anticipated crisis to Argentina 1994, Argentina 2001 anticipated to

México and United States 2007 anticipated to México july 2008.

Nevertheless, the analysis of financial crisis mentioned from the first, second and

third generation cannot be used as early systems to identify financial crises; the

results of these studies are diverse and remain a significant site for further

investigation, Liu & Lindholm (2007).

3.2 Financial Forecasting

A financial forecast is an estimate of future financial outcomes for a company or

country (for futures and currency markets). Using historical internal accounting and

sales data, in addition to external market and economic indicators, a financial

forecast is an economist's best guess of what will happen to a company in financial

terms over a given time period—which is usually one year.

Using the case of the currency crises as an important illustration of the financial

crises in general, this section presents a brief overview of the theoretical literature on

the causes of currency crises with a special reference to identifying the potential

early warning indicators. The historical development of the theoretical literature can

be grouped in three generations of models each reflecting the distinct mechanism that

is espoused as the major cause of such crises. We will discuss these models in turn.

Epitomized by Krugman (1979), the First Generation Models tend to focus on the

role of economic and financial fundamentals such as the unsustainable fiscal policies

in the face of the fixed exchange rate as the major cause of an eventual currency

crisis. Given a fixed exchange rate regime, the persistent need to finance

15

government budget deficits through monetization would surely lead to a reduction in

the international reserves held by the Central Bank. Since such reserves are finite the

speculative attack on the currency is the eventual outcome of this scenario. This

rather simple model suggests certain 'fundamental' imbalances such as the gradual

decline in international reserves, growing budget and current account deficits,

domestic credit growth, and gradual exchange rate overvaluation as the potential

early warning indicators of speculative attacks.

The development of the so-called Second Generation Models of the currency

crises were motivated by the EMS currency crisis in 1992-93 where some countries

such as the UK and Spain suffered crises despite having adequate international

reserves, manageable domestic credit growth and non-monetized fiscal deficits

characteristics that ran counter to the necessary conditions asserted by the first

generation models. Obstfeld (1994) and Krugman (1998) addressed the concerns

raised by these counter-examples. The main innovation of these Second Generation

Models lies in identifying the role that the 'expectations' of the market agents may

play in precipitating currency crises. These models allow for multiple equilibria and,

under certain circumstances of perfect information-based decision making, could

argue that predicting crises may not be feasible due to the 'self-fulfilling' nature of

the expectations of the crisis.

Finally, the Third Generation Models are based on the notion of 'contagion'

where the mere occurrence of a crisis in one country increases the likelihood of a

similar crisis elsewhere As described in Masson (1998), three related scenarios can

be identified to represent the paradigm of contagion: monsoonal effects, spillover

effects and pure contagion effects.7

It is shown below a cycle of traditional forecast process,

7 (Mariano, Gultekin, Ozmucur, Shabbir,t.y.:1-9)

16

Figure 5 - A cycle of traditional forecast process

17

4. APPLYING THE SIGNAL APPROACH TO THE 2008 CRISIS IN

TURKEY

4.1 Signal Approach

The signals approach, commonly associated with Kaminsky Lizondo and

Reinhart (1998), Goldstien, Kaminsky, and Reinhart (2000), Edison (2000), Berg and

Patillo (1999), in the literature, is the non-parametric approach in determining a

possible currency crisis. Additional signals approach models arise from Bruggemann

and Linne (2000), and the Asian Development Bank’s Non Parametric Early

Warning System (released in the literature in 2005).

A dummy crisis variable is constructed from an exchange market pressure, also

known as an index of speculative pressure (ISP) with a specified threshold. Also,

this approach constructs binary variables from each explanatory variable included.

The binary variable takes on a value of one, known to be a signal, once the variable

of interest exceeds a chosen threshold, and zero, otherwise. The rationale for such a

specification is that only severely abnormal behavior should be noted. The

explanatory variables come from a list of possible early warning indicator variables,

as suggested by economic theory. Then, this list is trimmed down by the availability

of data.

Once the binary dummy crisis variables have been identified through the

arbitrary specification, these signals are classified into four categories depending on

their ability to call a crisis. A signal is classified as a good signal if a crisis does

occur within a specified period, and a false signal otherwise. Likewise, a No signal

is classified as a good no signal if a crisis does not occur within the specified period.

Thresholds are chosen so as to strike a balance between the costs and benefits that

arise from having many false signals and the risk of missing many crisis.

Figure 6 – Signal approach table

18

In order to assess the value of each explanatory variable, a signal to noise ratio

SNR is calculated, although in some literature, this is known as a noise to signal

ratio. A SNR is computed for each explanatory variable over the sample period.

This is done by classifying each observation into one of the four categories as well.

The thresholds are chosen to maximize the SNR = [A/(A+C)]/[B/(B+D)].

This is the proportion of a crisis detected over the proportion of a false positive

signal. The indicators are ranked according to their own SNR. These signals are

then used in a non parametric setting by simply monitoring their behavior and

counting the number of indicators that is signaling a crisis. These signals may be

aggregated into a composite indicator by constructing a weighted average of the

signals, where the weights depend on the relative accuracy of the signals or the

rankings of their own SNR.

In this article, it is defined a crisis index called as combined crisis index, and

shown as CCI. CCI can be calculated as a function as macro economical indicators.

Below, it is shown the macro economical indicators that use in the calculation of

CCI.

Table 1- Selected macro economical indicators

SELECTED INDICATORS

1 Golden Prices

2 Employment Ratio

3 Currency Reserves

4 Automotive Production

5 Export

6 Import

7 Industry Production Index

8 Real Currency Rate

9 Unemployment Ratio

19

These variables are taken from TCMB electronics database system for the period

between January 2005-2012.8

In addition to these, the CCI is calculated as below formula for the period

between January 2005-2012, and it is reported as a crisis signal when CCI occurs 0,8

sigma below and upper from the average at a certain period between this time.

Mathematically it is shown here,

CCIt = 𝑊𝑡(Et− Ut)

∆t

𝑛

1

/∑𝑊 (4.1)

Where;

CCIt: Combined Crisis Index at a Certain Period

Et: Change in the indicator (%)

Ut: Average of the indicator (%)

∆t: Standard Deviation of the indicator (%)

N: Number of indicators

W: Weight of the indicator

The border value of an indicator can be shown as,

𝐵𝑉 = 𝑈𝑡 + 𝛼∆𝑡 (4.2)

Where;

BV: Border Value of Indicator

Ut: Average of the indicator (%)

α: Constant Coefficient

∆t: Standard Deviation of the indicator (%)

8 (Castillo, 2006:20-22)

20

𝑈𝑡 − 𝛼∆𝑡 > 𝐶𝐶𝐼𝑡 > 𝑈𝑡 + 𝛼∆𝑡 (4.3)

If this equation occurs, CCI=0, there is no signal

If it is not, CCI=1, Signal

In this article, combined crisis index calculated as a function of variables. The

constant coefficient accepted as 0,8 in this article.

To see the performance of combined crisis index, it is tested first all indicators

with same weight and see the performance. Second, it is used MS EXCEL Solver

program to determine optimal weights of indicators.

After these calculations, it ended by calculation of signal noise ratios both

indicators and combined crisis index.

At the last part of article, it is evaluated the performance of the combined crisis

index for the 2008 crisis Turkey.

4.2 Selection of the Macro Economical Indicators

In this section, it is calculated border values for each variable, and evaluate the

performance for the 2008 crisis Turkey.

Nine indicators are selected for this article: Golden prices, unemployment ratio,

automotive production, currency reserves, export, import, real currency rate,

employment ratio, industry production index. Month 1 is January 2005, and Month

84 is December 2011. Month 45 is September 2008, which can be accepted as

beginning of the 2008 crisis.

4.2.1 Golden Prices

Golden prices are selected due to effect of Turkish economy. It is known that

%25 of the total golden is not in the economy in Turkey. In addition to this, there is

a strong correlation between golden prices and oil prices.

At the table below, it is shown the golden prices between the periods of January

2005-2012, and golden prices have an upper boundary to show the crisis signal.

21

M1 is the coefficient to use in the calculation of combined crisis signal.

Table 2 – Golden Price Analysis

22

Table 3 – Golden Prices True Signal Ratio

Golden Prices

Alfa 0,8

A 3

B 11

C 9

D 60

True Signal Probability 25,00%

False Signal Probability 15,49%

Noise Signal Ratio 61,97%

The Probability of Crisis When There

is a Signal 21,43%

The Probability of Crisis When There

is a Signal-The Probability of Crisis 6,97%

True Signal Ratio 75,90%

As we see in the table, true signal ratio is 75,90% and there 11 false signals

between the period.

4.2.2 Employment Ratio

Employment ratio is selected due to effect of Turkish economy. It is known that

before the crisis period, it can be dismissals in business life.

At the table below, it is shown the employment ratios between the periods of

January 2005-2012, and employment ratio have a lower boundary to show the crisis

signal.

M8 is the coefficient to use in the calculation of combined crisis signal.

23

Table 4 – Employement Ratio Analysis

24

Table 5 - Employement True Signal Ratio

Employement Ratio

Alfa 0,8

A 1

B 9

C 11

D 62

True Signal Probability 8,33%

False Signal Probability 12,68%

Noise Signal Ratio 152,11%

The Probability of Crisis When There is a Signal 10,00%

The Probability of Crisis When There is a

Signal-The Probability of Crisis -4,46%

True Signal Ratio 75,90%

As we see in the table, true signal ratio is 75,90% and there 9 false signals

between the period.

4.2.3 Currency Reserves

Currency reserves are selected due to effect of Turkish economy. It is known

that before the crisis period, it can be drops at the currency reserves of central bank.

At the table below, it is shown the currency reserves between the periods of

January 2005-2012, and currency reserves have a lower boundary to show the crisis

signal.

M2 is the coefficient to use in the calculation of combined crisis signal.

25

Table 6 – Currency Reserves Analysis

26

Table 7 – Currency Reserves True Signal Ratio

Currency Reserves

Alfa 0,8

A 1

B 15

C 11

D 56

True Signal Probability 8,33%

False Signal Probability 21,13%

Noise Signal Ratio 253,52%

The Probability of Crisis When There is a

Signal 6,25%

The Probability of Crisis When There is a

Signal-The Probability of Crisis -8,21%

True Signal Ratio 68,67%

As we see in the table, true signal ratio is 68,67% and there 15 false signals

between the period.

4.2.4 Automotive Production

Automotive production is selected due to effect of Turkish economy. It is known

that Turkey is the one of most important European countries in this region.

At the table below, it is shown the automotive production between the periods of

January 2005-2012, and automotive production has a lower boundary to show the

crisis signal.

M3 is the coefficient to use in the calculation of combined crisis signal.

27

Table 8 – Automotive Production Analysis

28

Table 9 – Automotive Production True Signal Ratio

Automotive Production

Alfa 0,8

A 1

B 8

C 11

D 63

True Signal Probability 8,33%

False Signal Probability 11,27%

Noise Signal Ratio 135,21%

The Probability of Crisis When There is a

Signal 11,11%

The Probability of Crisis When There is a

Signal-The Probability of Crisis -3,35%

True Signal Ratio 77,11%

As we see in the table, true signal ratio is 77,11% and there 8 false signals

between the period.

4.2.5 Export

Export is selected due to effect of Turkish economy. It is known that export rate

can be drop before crisis.

At the table below, it is shown the export between the periods of January 2005-

2012, and export has a lower boundary to show the crisis signal.

M4 is the coefficient to use in the calculation of combined crisis signal.

29

Table 10 – Export Analysis

30

Table 11 – Export True Signal Ratio

Export

Alfa 0,8

A 2

B 9

C 10

D 62

True Signal Probability 16,67%

False Signal Probability 12,68%

Noise Signal Ratio 76,06%

The Probability of Crisis When There is a Signal 18,18%

The Probability of Crisis When There is a Signal-

The Probability of Crisis 3,72%

True Signal Ratio 77,11%

As we see in the table, true signal ratio is 77,11% and there 9 false signals

between the period.

4.2.6 Import

Import is selected due to effect of Turkish economy. It is known that import rate

can be increase before crisis.

At the table below, it is shown the import between the periods of January 2005-

2012, and export has an upper boundary to show the crisis signal.

M5 is the coefficient to use in the calculation of combined crisis signal.

31

Table 12 – Import Analysis

32

Table 13 – Import True Signal Ratio

Import

Alfa 0,8

A 6

B 12

C 6

D 59

True Signal Probability 50,00%

False Signal Probability 16,90%

Noise Signal Ratio 33,80%

The Probability of Crisis When There is a Signal 33,33%

The Probability of Crisis When There is a Signal-

The Probability of Crisis 18,88%

True Signal Ratio 78,31%

As we see in the table, true signal ratio is 78,31% and there 12 false signals

between the period.

4.2.7 Industry Production Index

Industry production index is selected due to effect of Turkish economy. It is

known that this index can be decrease before crisis.

At the table below, it is shown the industry production index between the periods

of January 2005-2012, and industry production index has a lower boundary to show

the crisis signal.

M6 is the coefficient to use in the calculation of combined crisis signal.

33

Table 14 – Industry Production Analysis

34

Table 15 – Industry Production True Signal Ratio

Industry Production Index

Alfa 0,8

A 2

B 13

C 10

D 58

True Signal Probability 16,67%

False Signal Probability 18,31%

Noise Signal Ratio 109,86%

The Probability of Crisis When There is a Signal 13,33%

The Probability of Crisis When There is a Signal-

The Probability of Crisis -1,12%

True Signal Ratio 72,29%

As we see in the table, true signal ratio is 72,29% and there 13 false signals

between the period.

4.2.8 Real Currency Rate

Real currency rate is selected due to effect of Turkish economy. It is known that

this rate can be decrease before crisis.

At the table below, it is shown the real currency rate between the periods of

January 2005-2012, and real currency rate has a lower boundary to show the crisis

signal.

M7 is the coefficient to use in the calculation of combined crisis signal.

35

Table 16 – Real Currency Analysis

36

Table 17 – Real Currency True Signal Ratio

Real Currency Rate

Alfa 0,8

A 2

B 11

C 10

D 60

True Signal Probability 16,67%

False Signal Probability 15,49%

Noise Signal Ratio 92,96%

The Probability of Crisis When There is a Signal 15,38%

The Probability of Crisis When There is a Signal-

The Probability of Crisis 0,93%

True Signal Ratio 74,70%

As we see in the table, true signal ratio is 74,70% and there 11 false signals

between the period.

4.2.9 Unemployment

Unemployment is selected due to effect of Turkish economy. It is known that

this rate can be decrease before crisis.

At the table below, it is shown the unemployment between the periods of January

2005-2012, and real currency rate has an upper boundary to show the crisis signal.

M9 is the coefficient to use in the calculation of combined crisis signal.

37

Table 18 – Unemployment Change Analysis

38

Table 19 – Unemployment True Signal Ratio

Unemployment

Alfa 0,8

A 3

B 13

C 9

D 58

True Signal Probability 25,00%

False Signal Probability 18,31%

Noise Signal Ratio 73,24%

The Probability of Crisis When There is a Signal 18,75%

The Probability of Crisis When There is a Signal-

The Probability of Crisis 4,29%

True Signal Ratio 73,49%

As we see in the table, true signal ratio is 73,49% and there 13 false signals

between the period.

5. COMBINED CRISIS INDEX

5.1 Combined Crisis Index with Same Weights

In this section of project, it is described a combined index, and it is named as

combined crisis index. This index is a function of 9 macro economical variables.

Firstly, it is tested the performance of combined crisis index with same weighted

variables. It is calculated M coefficients for all 9 variables, and then with the

formula that given in the methodology section CCI is calculated for the between

January 2005-2012 months.

It is accepted that when CCI is under or over 0,8 sigma, this is a crisis signal for

the next 12 months.

39

Below, it is shown the CCI values of this between the periods January 2005-

2012.

Table 20 - CCI values of this between the periods January 2005-2012

M1 M2 M3 M4 M5 M6 M7 M8 M9

Wi 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00

Wi: Weights of M coefficients.

40

Table 21 – Combined Crisis Index Analysis

41

Figure 7 – Combined Crisis Index Change

Table 22 – Combined Crisis Index True Signal Ratio

Combined Crisis Index

Alfa 0,8

A 1

B 11

C 11

D 60

True Signal Probability 8,33%

False Signal Probability 15,49%

Noise Signal Ratio 185,92%

The Probability of Crisis When There is a Signal 8,33%

The Probability of Crisis When There is a Signal-

The Probability of Crisis -6,12%

True Signal Ratio 73,49%

-2500,00%

-2000,00%

-1500,00%

-1000,00%

-500,00%

0,00%

500,00%

1000,00%

Mo

nth

s 4 8

12

16

20

24

28

32

36

40

44

48

52

56

60

64

68

72

76

80

Combined Crisis Index

"Combined Crisis Index Change %"

"Combined Crisis Index Change Lower Boundary"

"Combined Crisis Index Change Upper Boundary"

42

As we see in the table, true signal ratio is 73,49% and there 11 false signals

between the period. This is not a good result for a crisis modeling with a combined

index. This is a result of determining same weights for variables.

Next section, MS EXCEL Solver addition will be use for determining the weights

of M coefficient.

5.2 Recalculating Combined Crisis Index

In this section, it is calculated weights of M coefficients with MS EXCEL Solver

addition to maximize the true signal ratio of combined crisis index for the

economical recession in September, 2008.

After the solution of solver, below you can see the new weights of the M

coefficients.

Table 23 - CCI values of this between the periods January 2005-2012 after recalculating

M1 M2 M3 M4 M5 M6 M7 M8 M9

Wi 19,34 79,13 -27,57 18,17 178,09 48,78 51,88 36,88 57,86

After this calculation below you can see the combined crisis values for the period

January 2005-2012.

43

Table 24 – Combined Crisis Index Analysis After Recalculation

At the table it can seen that May and June 2008 there are two signals for the

coming crisis. Other periods have no signal for a possible crisis.

44

Table 25 – Combined Crisis Index True Signal Ratio After Recalculation

Combined Crisis Index

Alfa 0,8

A 2

B 0

C 10

D 71

True Signal Probability 16,67%

False Signal Probability 0,00%

Noise Signal Ratio 0,00%

The Probability of Crisis When There is a Signal 100,00%

The Probability of Crisis When There is a Signal-

The Probability of Crisis 85,54%

True Signal Ratio 87,95%

As we see in the table, true signal ratio is 87,95% and there 0 false signals

between the period. This is a good result for a crisis modeling with a combined

index. This is a result of determining weights for variables with using MS EXCEL

Solver addition.

6. RESULTS

6.1 Evaluation of the Results

As a result of this article, firstly, it is determined the indicators for calculation of

a combined crisis index. The index is determined as a function of golden prices,

unemployment ratio, automotive production, currency reserves, real currency rate,

employment ratio, import, export and industry production index.

Secondly, a combined crisis index is calculated by same weighted use of

indicators for the period of January 2005-2012. Then, it is tested performance of the

combined crisis index under these conditions.

45

Thirdly, the combined crisis index is recalculated by MS EXCEL Solver

addition, and with these new weights, index gives two signals before the 2008

September crisis.

Here in the results section, it is shown the graphs below the evaluation of project.

Table 26 – Comparison of Nine Indicators and Combined Crisis Index

Above, it is easy to see that a combined index has better performance than a

single indicator for an early warning system. True signal ratio is higher than the

other variables.

Figure 8 - Combined Crisis Index After Calculation

-180000,00%

-160000,00%

-140000,00%

-120000,00%

-100000,00%

-80000,00%

-60000,00%

-40000,00%

-20000,00%

0,00%

20000,00%

Mo

nth

s

16

20

24

28

32

36

40

44

48

52

56

60

64

68

72

76

80

Combined Crisis Index

"Combined Crisis Index Change %"

"Combined Crisis Index Change Lower Boundary"

"Combined Crisis Index Change Upper Boundary"

46

Above the graph, it is shown the early warning signal between the months 38-42.

For an early warning system model, the performance of the model for another

recession is important. In the April, 2006 there was a recession in European

economy, and it has some affects over Turkey’s economy.

Table 27 – The values of Combined Crisis Index in the year 2006

Above table, it is shown that 2006 values of combined crisis index. As you can

see in the table the months 18, the change in the index -2179,74%. This is the affects

of recession on the European finance environment.

To sum up, it is possible to determine a crisis prediction model in many ways.

KLR or signal approach is used in this article. At the end of article, a model is

determined with MS EXCEL Solver addition.

The above sections suggest, it is better watching a combined index instead of one

indicator to predict an economical crisis.

47

6.2 Reliability of the Indicators

In the Attachment A, it can be seen that the multivariable regression analysis of

the selected indicators. In this analysis, dependent variable is selected as CCI and the

other ones are independent variables.

6.3 Assessment of the Year 2012

As shown on the tables at Chapter 4, CCI gives two signals before the September

2008 crisis. Similarly, if this approach is applied for the year 2012, it can be seen that

no signal is detected. It is shown at the table below.

Table 28 - Combined Crisis Index Analysis After Recalculation for the Year 2012

To sum up, it is clearly seen that, no signal is detected for the year 2012.

Months

Combined

Crisis Index

Combined Crisis

Index Change

Combined Crisis Index Change

% Lover Boundary

Combined Crisis Index Change

% Upper Boundary

Combined Crisis Index

Change % Avarege

Combined Crisis Index

Change % Deviation Signal

72 20,43% -278,27% -15879,28% 11505,42% -2186,93% 17115,44% No signal

73 49,01% 139,83% -15879,28% 11505,42% -2186,93% 17115,44% No signal

74 5,19% -89,42% -15879,28% 11505,42% -2186,93% 17115,44% No signal

75 63,68% 1128,21% -15879,28% 11505,42% -2186,93% 17115,44% No signal

76 59,33% -6,84% -15879,28% 11505,42% -2186,93% 17115,44% No signal

77 33,17% -44,09% -15879,28% 11505,42% -2186,93% 17115,44% No signal

78 -21,04% -163,42% -15879,28% 11505,42% -2186,93% 17115,44% No signal

79 -24,47% 16,29% -15879,28% 11505,42% -2186,93% 17115,44% No signal

80 -22,07% -9,80% -15879,28% 11505,42% -2186,93% 17115,44% No signal

81 -78,16% 254,17% -15879,28% 11505,42% -2186,93% 17115,44% No signal

82 -14,56% -81,37% -15879,28% 11505,42% -2186,93% 17115,44% No signal

83 -19,41% 33,33% -15879,28% 11505,42% -2186,93% 17115,44% No signal

84 -47,44% 144,43% -15879,28% 11505,42% -2186,93% 17115,44% No signal

48

ATTACHMENT-A

SUMMARY OUTPUT

Regression Statistics

Multiple R 0,511440148

R Square 0,261571025

Adjusted R Square 0,170531836

Standard Error 0,473535897

Observations 83

ANOVA

df SS MS F Significance F

Regression 9 5,798418782 0,644268754 2,873169552 0,005851886

Residual 73 16,36924594 0,224236246

Total 82 22,16766472

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%

Intercept -2,901464577 1,407546868 -2,061362675 0,04283031 -5,706701399 -0,096227754 -5,706701399 -0,096227754

13100000 -6,9798E-09 1,70979E-08 -0,408225362 0,684303168 -4,10559E-08 2,70963E-08 -4,10559E-08 2,70963E-08

85195850000 2,55038E-14 7,07688E-14 0,360381414 0,719602588 -1,15538E-13 1,66546E-13 -1,15538E-13 1,66546E-13

3712870000 7,42145E-11 1,09758E-10 0,676165222 0,501072435 -1,44533E-10 2,92962E-10 -1,44533E-10 2,92962E-10

2723500000 -2,47055E-11 6,07794E-11 -0,406478031 0,685580615 -1,45839E-10 9,64277E-11 -1,45839E-10 9,64277E-11

12537000 4,92563E-07 1,91006E-07 2,578778398 0,011929652 1,11888E-07 8,73239E-07 1,11888E-07 8,73239E-07

12531000 -5,06027E-07 1,64794E-07 -3,070659023 0,002999252 -8,34462E-07 -1,77593E-07 -8,34462E-07 -1,77593E-07

8373000 1,78825E-07 1,02181E-07 1,750075456 0,084307184 -2,4822E-08 3,82471E-07 -2,4822E-08 3,82471E-07

10860000 1,22509E-07 9,2736E-08 1,321051917 0,190610119 -6,23135E-08 3,07331E-07 -6,23135E-08 3,07331E-07

1180000 -2,07873E-07 5,39028E-07 -0,385643815 0,700882013 -1,28215E-06 8,66409E-07 -1,28215E-06 8,66409E-07

49

ATTACHMENT-B

Months Golden Prices Employement Ratio Currency Reserves Automotive Production Export Import Industry Production Index Real Currency Rate Unemployement Ratio

1 13100000 85.195.850.000 3712870000 2723500000 12537000 12.531.000 8.373.000 10.860.000 1.180.000

2 12800000 89.802.530.000 3667270000 3362700000 12494000 12.421.000 8.598.000 11.206.000 1.190.000

3 12850000 94.800.260.000 3802480000 4218800000 12694000 12.594.000 9.850.000 11.113.000 1.120.000

4 13320000 100.975.620.000 3731990000 3962400000 12509000 12.661.000 9.500.000 10.906.000 1.040.000

5 12650000 111.471.530.000 3583470000 4451200000 12502000 12.456.000 9.910.000 11.009.000 960.000

6 12950000 110.702.040.000 3996220000 4515000000 12402000 12.220.000 10.232.000 11.407.000 960.000

7 12620000 112.489.900.000 4289520000 3816400000 12271000 12.157.000 9.995.000 11.620.000 960.000

8 13000000 118.762.220.000 4117440000 2286400000 12274000 12.258.000 9.986.000 11.457.000 990.000

9 13540000 119.017.340.000 4176890000 4639600000 12402000 12.467.000 10.899.000 11.553.000 1.010.000

10 13875000 121.863.510.000 4482990000 4229300000 12160000 12.458.000 11.144.000 11.765.000 1.040.000

11 14433000 124.798.710.000 4788360000 3385500000 12077000 12.281.000 10.206.000 12.093.000 1.100.000

12 14920000 136.100.700.000 5051520000 3775500000 12051000 12.378.000 11.303.000 12.121.000 1.150.000

13 16333000 167.034.400.000 5292920000 2935400000 12471000 12.784.000 8.469.000 12.147.000 1.210.000

14 16550000 177.657.600.000 5653380000 3845700000 12350000 12.683.000 9.363.000 12.276.000 1.220.000

15 16500000 190.624.400.000 5828310000 5183300000 12504000 12.796.000 11.059.000 12.163.000 1.130.000

16 18250000 209.340.100.000 5975270000 4838700000 12706000 13.215.000 10.484.000 12.076.000 1.030.000

17 22125000 208.288.200.000 5971380000 5346500000 13054000 13.669.000 11.154.000 11.192.000 920.000

18 21300000 218.357.900.000 5673240000 5529800000 13022000 13.540.000 11.326.000 10.024.000 920.000

19 21725000 237.536.600.000 5701410000 5233700000 13017000 13.777.000 10.933.000 10.363.000 930.000

20 20275000 248.993.900.000 5798440000 1831500000 13039000 14.064.000 10.637.000 10.842.000 960.000

21 19820000 273.437.500.000 5852590000 5346500000 12694000 13.771.000 11.397.000 10.940.000 950.000

22 19100000 289.088.300.000 5747990000 4554200000 12617000 13.696.000 10.710.000 11.096.000 960.000

23 20250000 296.266.300.000 5820760000 5371000000 12812000 13.648.000 11.667.000 11.248.000 1.000.000

24 19700000 314.898.500.000 6091240000 4551900000 13264000 14.113.000 11.587.000 11.201.000 1.090.000

25 19600000 338.761.600.000 6303590000 4053300000 13440000 13.690.000 10.095.000 11.491.000 1.130.000

26 20625000 360.662.400.000 6466080000 4541500000 13510000 13.770.000 10.198.000 11.685.000 1.170.000

27 20000000 383.536.400.000 6749760000 5390900000 13680000 14.020.000 11.740.000 11.539.000 1.070.000

28 20200000 402.950.500.000 6731550000 5322800000 13960000 14.310.000 11.110.000 11.853.000 1.010.000

29 19850000 399.425.400.000 6628100000 6212900000 14220000 14.580.000 11.982.000 12.047.000 920.000

30 19040000 419.940.900.000 6825190000 5990200000 14230000 14.490.000 11.863.000 12.209.000 920.000

31 19575000 456.225.400.000 6997560000 5627300000 14600000 14.890.000 11.600.000 12.257.000 930.000

32 19860000 492.558.800.000 7187540000 2361400000 14390000 14.740.000 11.483.000 11.996.000 970.000

33 19750000 518.225.000.000 7172510000 5581800000 14560000 14.960.000 11.887.000 12.373.000 990.000

34 19950000 556.087.000.000 7227900000 5946300000 15080000 15.550.000 11.800.000 12.975.000 1.020.000

35 20820000 577.876.800.000 7241260000 6860800000 15570000 16.100.000 12.633.000 12.995.000 1.050.000

36 20500000 628.342.500.000 7331710000 5599100000 15830000 16.290.000 11.404.000 13.166.000 1.090.000

37 21975000 663.768.900.000 7434750000 6150700000 15990000 16.630.000 11.230.000 13.171.000 1.160.000

38 23460000 701.181.800.000 7490970000 6086800000 16190000 16.810.000 11.132.000 13.000.000 1.190.000

39 25100000 737.946.700.000 7647640000 6497300000 16870000 17.470.000 12.153.000 12.143.000 1.100.000

40 25925000 787.086.200.000 7466020000 6561000000 17210000 18.200.000 11.944.000 11.553.000 990.000

41 24460000 797.927.900.000 7405220000 6393100000 17400000 18.410.000 12.340.000 12.241.000 920.000

42 24750000 838.959.100.000 7590820000 6311800000 17820000 19.100.000 12.139.000 12.315.000 940.000

43 24975000 899.624.300.000 7582780000 6789100000 18510000 19.790.000 12.108.000 12.352.000 990.000

44 22960000 941.571.500.000 7593380000 2134200000 18050000 19.240.000 11.093.000 13.185.000 1.020.000

45 22925000 994.745.600.000 7752080000 5508900000 17590000 18.590.000 11.414.000 13.041.000 1.070.000

46 25620000 1.020.994.800.000 7200510000 4505700000 15610000 17.480.000 11.051.000 11.814.000 1.120.000

47 25825000 1.068.350.200.000 7120480000 3168100000 14370000 15.960.000 11.004.000 11.525.000 1.260.000

48 26227000 1.161.288.500.000 7100760000 2050000000 14330000 15.250.000 9.418.000 11.490.000 1.400.000

49 29560000 1.252.724.500.000 6796160000 2383800000 13740000 14.350.000 8.810.000 11.377.000 1.550.000

50 33650000 1.314.692.000.000 6754720000 3019200000 13140000 14.070.000 8.463.000 11.269.000 1.610.000

51 34175000 1.424.444.600.000 6712070000 3712500000 12980000 14.000.000 9.543.000 10.948.000 1.580.000

52 31150000 1.536.262.200.000 6432950000 4856600000 13240000 13.300.000 9.694.000 11.404.000 1.490.000

53 31500000 1.600.501.200.000 6737800000 5263100000 13550000 13.410.000 10.231.000 11.520.000 1.360.000

54 31925000 1.697.971.500.000 6586110000 5357700000 13920000 13.890.000 10.936.000 11.342.000 1.300.000

55 31200000 1.855.358.700.000 6697020000 5487300000 14030000 14.000.000 11.035.000 11.528.000 1.280.000

56 31125000 1.920.882.700.000 7036860000 2380500000 14250000 14.330.000 10.373.000 11.612.000 1.340.000

57 32300000 2.002.836.800.000 7091770000 5032000000 14470000 14.500.000 10.314.000 11.475.000 1.340.000

58 33620000 2.164.140.700.000 7154020000 4545200000 14660000 15.060.000 11.737.000 11.735.000 1.300.000

59 36000000 2.267.288.800.000 7151020000 4163500000 14920000 15.330.000 10.676.000 11.650.000 1.310.000

60 36775000 2.292.014.500.000 7071590000 4891700000 14760000 15.120.000 11.671.000 11.680.000 1.350.000

50

61 35450000 2.418.731.100.000 7077850000 4681000000 14780000 15.280.000 9.934.000 12.288.000 1.450.000

62 36300000 2.588.202.500.000 6772110000 4636500000 14400000 15.060.000 9.950.000 12.427.000 1.440.000

63 37100000 2.667.914.400.000 6944420000 5575500000 14430000 15.240.000 11.582.000 12.316.000 1.370.000

64 37320000 2.853.876.900.000 7295060000 5145900000 14640000 15.600.000 11.330.000 12.755.000 1.200.000

65 40375000 2.984.027.900.000 7229890000 5743300000 14440000 15.340.000 11.760.000 12.836.000 1.100.000

66 41700000 3.039.162.000.000 7101230000 5466000000 14150000 15.000.000 12.027.000 12.764.000 1.050.000

67 40180000 3.094.379.300.000 7421520000 4751600000 14440000 15.070.000 12.005.000 12.585.000 1.060.000

68 40550000 3.139.298.600.000 7657390000 2919200000 14360000 15.230.000 11.498.000 12.721.000 1.140.000

69 41633000 3.141.679.400.000 7766480000 5404600000 14560000 15.360.000 11.386.000 12.861.000 1.130.000

70 41775000 3.223.701.000.000 7907160000 5805900000 15190000 16.130.000 12.898.000 13.135.000 1.120.000

71 42400000 3.251.724.100.000 7910880000 4545300000 15260000 16.370.000 11.678.000 13.101.000 1.100.000

72 46100000 3.642.062.000.000 8072070000 5664600000 15280000 16.340.000 13.620.000 12.571.000 1.140.000

73 46575000 4.442.810.600.000 8288840000 5146100000 15840000 16.770.000 11.837.000 12.119.000 1.190.000

74 47375000 4.542.748.800.000 8306800000 5446200000 16140000 17.270.000 11.342.000 11.735.000 1.150.000

75 48700000 5.085.118.400.000 8679600000 6287300000 16530000 17.780.000 12.807.000 11.587.000 1.080.000

76 48200000 5.920.908.100.000 9022110000 5409600000 16920000 18.370.000 12.343.000 11.833.000 990.000

77 51150000 8.454.560.400.000 9186150000 5419700000 16800000 18.300.000 12.720.000 11.724.000 940.000

78 52975000 9.033.170.200.000 9373760000 5869400000 16880000 18.240.000 12.865.000 11.330.000 920.000

79 56480000 9.639.522.200.000 9301390000 5739500000 16840000 18.280.000 12.845.000 10.937.000 910.000

80 68300000 10.181.410.800.000 8907140000 2613200000 16660000 18.510.000 11.937.000 10.351.000 920.000

81 69340000 10.576.851.100.000 8753700000 5742700000 16150000 18.070.000 12.761.000 10.491.000 880.000

82 65930000 10.930.570.300.000 8477860000 6017200000 15970000 18.080.000 13.866.000 10.670.000 910.000

83 67425000 11.724.476.700.000 8506730000 5175900000 15920000 17.920.000 12.672.000 11.047.000 910.000

84 66480000 12.215.726.000.000 7845820000 5106600000 15800000 17.710.000 14.126.000 10.952.000 980.000

51

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