Prediction of economical recession with the signal approach, and the turkey case

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

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.

iii

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

iv

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

v

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

vi

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

vii

Table 28 - Combined Crisis Index Analysis After Recalculation for the Year
2012 ............................................................................................................................ 47

viii

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

ix

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

1

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)

2

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 No Crisis
(Within 24 months) (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)

3

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,

4

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.

5

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.

6

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)

7

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)

8

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

9

£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

10

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)

11

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.

12

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

13

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

14

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)

15

Figure 5 - A cycle of traditional forecast process

16

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

17

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

18

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,

𝑛
(Et − Ut)
CCIt = 𝑊𝑡 /∑𝑊 (4.1)
∆t
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)

19

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.

20

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

Table 2 – Golden Price Analysis

21

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.


22

Table 4 – Employement Ratio Analysis

23

Table 5 - Employement True Signal Ratio

Employement Ratio

Alfa 0,8

A 1

B 9

C 11

D 62




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%


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.


24

Table 6 – Currency Reserves Analysis

25

Table 7 – Currency Reserves True Signal Ratio

Currency Reserves

Alfa 0,8

A 1

B 15

C 11

D 56




Signal 6,25%



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.


26

Table 8 – Automotive Production Analysis

27

Table 9 – Automotive Production True Signal Ratio

Automotive Production

Alfa 0,8

A 1

B 8

C 11

D 63




Signal 11,11%



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.


28

Table 10 – Export Analysis

29

Table 11 – Export True Signal Ratio

Export

Alfa 0,8

A 2

B 9

C 10

D 62





The Probability of Crisis When There is a Signal-
The Probability of Crisis 3,72%


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.


30

Table 12 – Import Analysis

31

Table 13 – Import True Signal Ratio

Import

Alfa 0,8

A 6

B 12

C 6

D 59







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.


32

Table 14 – Industry Production Analysis

33

Table 15 – Industry Production True Signal Ratio

Industry Production Index

Alfa 0,8

A 2

B 13

C 10

D 58





The Probability of Crisis -1,12%


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.


34

Table 16 – Real Currency Analysis

35

Table 17 – Real Currency True Signal Ratio

Real Currency Rate

Alfa 0,8

A 2

B 11

C 10

D 60







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.


36

Table 18 – Unemployment Change Analysis

37

Table 19 – Unemployment True Signal Ratio

Unemployment

Alfa 0,8

A 3

B 13

C 9

D 58







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.

38

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.

39

Table 21 – Combined Crisis Index Analysis

40

Combined Crisis Index
1000,00%

500,00%

0,00% Months
4
8

56
12
16
20
24
28
32
36
40
44
48
52

60
64
68
72
76
80
-500,00%

-1000,00%

-1500,00%

-2000,00%

-2500,00%

"Combined Crisis Index Change %"
"Combined Crisis Index Change Lower Boundary"
"Combined Crisis Index Change Upper Boundary"

Figure 7 – Combined Crisis Index Change

Table 22 – Combined Crisis Index True Signal Ratio


Alfa 0,8

A 1

B 11

C 11

D 60





The Probability of Crisis -6,12%


41

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.

42

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.

43

Table 25 – Combined Crisis Index True Signal Ratio After Recalculation


Alfa 0,8

A 2

B 0

C 10

D 71







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.

44

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.

20000,00%
0,00%
Months

72
16
20
24
28
32
36
40
44
48
52
56
60
64
68

76
80

-20000,00%
-40000,00%
-60000,00%
-80000,00%
-100000,00%
-120000,00%
-140000,00%
-160000,00%
-180000,00%

"Combined Crisis Index Change %"
"Combined Crisis Index Change Lower Boundary"
"Combined Crisis Index Change Upper Boundary"

Figure 8 - Combined Crisis Index After Calculation

45

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.

46

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

Combined Combined Crisis Combined Crisis Index Change Combined Crisis Index Change Combined Crisis Index Combined Crisis Index
Months Crisis Index Index Change % Lover Boundary % Upper Boundary Change % Avarege 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

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

47

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

48

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

49

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

50

7. REFERENCES

Türk, Armağan: "Predicting Financial Crisis with Artificial Neural Network
Model: the Case of Turkey", Research Journal of International Studies - Issue 21,
October, 2011, pp.68-79.

Peng, Duan., Bajona, Claustre: ―China’s Vulnerability to Currency Crisis: A
KLR Signals Approach‖, Master Thesis, Ryerson University, 2006.

Palmerin, Saul Toscano: ―Understanding Financial Crises‖, August 2012

Singh, Thangjam Rajeshwar: ―Ordered Probit model of Early Warning System
for Predicting Financial Crisis in India‖, Reserve Bank of India, Mumbai, 2011

Bordo, Micheal D., Landon-Lane, John S.: ―The Global Financial Crisis: Is It
Unprecedented?‖, Conference on Global Economic Crisis: Impacts, Transmission,
and Recovery, August 2010

Ramírez, D.; D. Ceballos; K. Cortez and M. Rodríguez: ―Financial Crisis: Theory
And Practice‖, June 2008

Mariano, Roberto S.; Gultekin, Bulent N.; Ozmucur, Suleyman; Shabbir, Tayyeb:
―Models of Economic and Financial Crises‖, Review of Economic Studies, April, 48,
pp. 289-309.

Fernando Antonio IV Castillo, ―Predicting a Currency Crisis Alternative
Approaches and Applications to the Philippines‖, Institutional Knowledge at
Singapore Management University, Dissertations and Theses Collection, 2006.

Sevim, Cüneyt: ―Öncü Göstergeler Yaklaşımına Göre Finansal Krizler ve
Türkiye Örneği‖, BDDK Kitapları No: 11, ISBN 978‐975‐9004‐16‐3, 2012.

Altıntaş, Halil; Öz, Bülent: ―Para Krizlerinin Sinyal Yaklaşımı Ile
Öngörülebilirliği: Türkiye Uygulaması‖, Anadolu Üniversitesi Sosyal Bilimler
Dergisi, Vol.7, No.2, 2007, pp.19-44

51

Mariano, Roberto S.; Gültekin, Bülent N.; Özmucur, Süleyman; Shabbir Tayyeb;
Alper, C. Emre: ―Prediction of currency crises: case of Turkey‖, Review of Middle
East Economics and Finance ISSN 1475-3685, Taylor & Francis Ltd, 2004.

―Financial Crisis‖, (Online), http://en.wikipedia.org/wiki/Financial_crisis

―Financial System‖, (Online), http://en.wikipedia.org/wiki/Financial_system

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