KLR Approach as an Early Warning Indicator of Turkish Currency and Banking Crisis in 2000 and 2001

KLR Approach as an Early Warning Indicator of Turkish Currency and Banking Crisis in 2000 and 2001

Filiz Eryılmaz (Uludağ University, Turkey)
DOI: 10.4018/978-1-4666-9484-2.ch011
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Abstract

International organizations as private sector institutions started to develop Early Warning System [EWS] models aiming to anticipate whether and when individual countries can collide with a financial crisis. EWS models can be made most useful to help sustain global growth and maintain financial stability, especially in light of the lessons learned from the current and past crises. This paper proposes Early Warning Systems (EWS) for Turkish Currency and Banking Crisis in 2000 and 2001. To that end “KLR model” or “signaling window” approach developed by Kaminski, Lorezondo and Reinhart (1998) is testified in the empirical part of this research and applied to a sample of Turkey macroeconomic data for the 1998-2003 monthly periods.
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1. Introduction

After 1980 in Turkey, as a result of the negative outcomes of the January 24th resolutions, the first exchange rate crisis was experienced in 1994. The most significant characteristic of the 1994 crisis was that the exchange rate crisis occurred together with an intense financial sector crisis. The reason for the economy going into crisis in 1994 was that the “April 5th Economic Resolutions” had to be implemented. Despite these resolutions, as structural transformation had not been made in the economy in the long term and macro economic stability had not been achieved, for the second time since 24th January 1980, the Turkish economy went into recession at the end of 2000 and in the beginning of 2001. Although improvement was achieved in some macro variables with the implementation of the “Inflation Reduction Program (IRP)”, supported by the IMF, the basic economic indicators remained weak in the measurement of the possibility of a speculative attack.

Therefore, following the 1994 crisis, the Turkish economy was shaken by a new crisis on 22nd November 2000. This was a crisis originating in the financial system with the leading actors in banking sector. Following the crisis of November 2000, the markets experienced a new and deeper shock in February 2001. The crisis that started in November 2000 in the banking sector became an exchange rate crisis on 19th February 2001 and thus had the characteristics of a twin crisis. The 2001 financial crisis resulted in the shrinking of the economy to an unexpected degree and brought about multi-dimensional new conditions, which changed the mid-term perspective of Turkey. Following the February 2001 crisis, Turkey entered the IMF-supported ‘Transition to a Strong Economy Program (TSEP)’ on 15th May 2001. The TSEP projected legislation in 15 areas to restructure the financial sector, increase the transparency of the State, strengthen public financing, increase competition and activity in the economy, and strengthen social consultation.

Key Terms in this Chapter

Regression Analysis: In statistics AU111: The URL http://en.wikipedia.org/wiki/Statistics has been redirected to https://en.wikipedia.org/wiki/Statistics. Please verify the URL. , regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable AU112: The URL http://en.wikipedia.org/wiki/Dependent_variable has been redirected to https://en.wikipedia.org/wiki/Dependent_variable. Please verify the URL. and one or more independent variables AU113: The URL http://en.wikipedia.org/wiki/Independent_variable has been redirected to https://en.wikipedia.org/wiki/Independent_variable. Please verify the URL. . More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation AU114: The URL http://en.wikipedia.org/wiki/Conditional_expectation has been redirected to https://en.wikipedia.org/wiki/Conditional_expectation. Please verify the URL. of the dependent variable given the independent variables – that is, the average value AU115: The URL http://en.wikipedia.org/wiki/Average_value has been redirected to https://en.wikipedia.org/wiki/Average_value. Please verify the URL. of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile AU116: The URL http://en.wikipedia.org/wiki/Quantile has been redirected to https://en.wikipedia.org/wiki/Quantile. Please verify the URL. , or other location parameter AU117: The URL http://en.wikipedia.org/wiki/Location_parameter has been redirected to https://en.wikipedia.org/wiki/Location_parameter. Please verify the URL. of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function AU118: The URL http://en.wikipedia.org/wiki/Function_(mathematics) has been redirected to https://en.wikipedia.org/wiki/Function_(mathematics). Please verify the URL. of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution AU119: The URL http://en.wikipedia.org/wiki/Probability_distribution has been redirected to https://en.wikipedia.org/wiki/Probability_distribution. Please verify the URL. . Regression analysis is widely used for prediction AU120: The URL http://en.wikipedia.org/wiki/Prediction has been redirected to https://en.wikipedia.org/wiki/Prediction. Please verify the URL. and forecasting AU121: The URL http://en.wikipedia.org/wiki/Forecasting has been redirected to https://en.wikipedia.org/wiki/Forecasting. Please verify the URL. , where its use has substantial overlap with the field of machine learning AU122: The URL http://en.wikipedia.org/wiki/Machine_learning has been redirected to https://en.wikipedia.org/wiki/Machine_learning. Please verify the URL. . Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships AU123: The URL http://en.wikipedia.org/wiki/Causality has been redirected to https://en.wikipedia.org/wiki/Causality. Please verify the URL. between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable; for example, correlation does not imply causation AU124: The URL http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation has been redirected to https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation. Please verify the URL. . Many techniques for carrying out regression analysis have been developed. Familiar methods such as linear regression AU125: The URL http://en.wikipedia.org/wiki/Linear_regression has been redirected to https://en.wikipedia.org/wiki/Linear_regression. Please verify the URL. and ordinary least squares AU126: The URL http://en.wikipedia.org/wiki/Ordinary_least_squares has been redirected to https://en.wikipedia.org/wiki/Ordinary_least_squares. Please verify the URL. regression are parametric AU127: The URL http://en.wikipedia.org/wiki/Parametric_statistics has been redirected to https://en.wikipedia.org/wiki/Parametric_statistics. Please verify the URL. , in that the regression function is defined in terms of a finite number of unknown parameters AU128: The URL http://en.wikipedia.org/wiki/Parameter has been redirected to https://en.wikipedia.org/wiki/Parameter. Please verify the URL. that are estimated from the data AU129: The URL http://en.wikipedia.org/wiki/Data has been redirected to https://en.wikipedia.org/wiki/Data. Please verify the URL. . Nonparametric regression AU130: The URL http://en.wikipedia.org/wiki/Nonparametric_regression has been redirected to https://en.wikipedia.org/wiki/Nonparametric_regression. Please verify the URL. refers to techniques that allow the regression function to lie in a specified set of functions AU131: The URL http://en.wikipedia.org/wiki/Function_(mathematics) has been redirected to https://en.wikipedia.org/wiki/Function_(mathematics). Please verify the URL. , which may be infinite-dimensional AU132: The URL http://en.wikipedia.org/wiki/Dimension has been redirected to https://en.wikipedia.org/wiki/Dimension. Please verify the URL. . The performance of regression analysis methods in practice depends on the form of the data generating process AU133: The URL http://en.wikipedia.org/wiki/Data_generating_process has been redirected to https://en.wikipedia.org/wiki/Data_generating_process. Please verify the URL. , and how it relates to the regression approach being used. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a sufficient quantity of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small effects AU134: The URL http://en.wikipedia.org/wiki/Effect_size has been redirected to https://en.wikipedia.org/wiki/Effect_size. Please verify the URL. or questions of causality based on observational data AU135: The URL http://en.wikipedia.org/wiki/Observational_study has been redirected to https://en.wikipedia.org/wiki/Observational_study. Please verify the URL. , regression methods can give misleading results.

Logit Model: In statistics AU80: The URL http://en.wikipedia.org/wiki/Statistics has been redirected to https://en.wikipedia.org/wiki/Statistics. Please verify the URL. , logistic regression, or logit regression, or logit model is a type of probabilistic statistical classification AU81: The URL http://en.wikipedia.org/wiki/Statistical_classification has been redirected to https://en.wikipedia.org/wiki/Statistical_classification. Please verify the URL. model. It is also used to predict a binary response from a binary predictor AU82: The URL http://en.wikipedia.org/wiki/Binary_classification has been redirected to https://en.wikipedia.org/wiki/Binary_classification. Please verify the URL. , used for predicting the outcome of a categorical AU83: The URL http://en.wikipedia.org/wiki/Categorical_variable has been redirected to https://en.wikipedia.org/wiki/Categorical_variable. Please verify the URL. dependent variable AU84: The URL http://en.wikipedia.org/wiki/Dependent_and_independent_variables has been redirected to https://en.wikipedia.org/wiki/Dependent_and_independent_variables. Please verify the URL. (i.e., a class label) based on one or more predictor variables (features). That is, it is used in estimating the parameters of aqualitative response model AU85: The URL http://en.wikipedia.org/wiki/Qualitative_response_models has been redirected to https://en.wikipedia.org/wiki/Qualitative_response_models. Please verify the URL. . The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function AU86: The URL http://en.wikipedia.org/wiki/Logistic_function has been redirected to https://en.wikipedia.org/wiki/Logistic_function. Please verify the URL. . Frequently (and hereafter in this article) “logistic regression” is used to refer specifically to the problem in which the dependent variable is binary AU87: The URL http://en.wikipedia.org/wiki/Binary_variable has been redirected to https://en.wikipedia.org/wiki/Binary_variable. Please verify the URL. —that is, the number of available categories is two—while problems with more than two categories are referred to as multinomial logistic regression AU88: The URL http://en.wikipedia.org/wiki/Multinomial_logistic_regression has been redirected to https://en.wikipedia.org/wiki/Multinomial_logistic_regression. Please verify the URL. or, if the multiple categories are ordered as ordered logistic regression AU89: The URL http://en.wikipedia.org/wiki/Ordered_logistic_regression has been redirected to https://en.wikipedia.org/wiki/Ordered_logistic_regression. Please verify the URL. . Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables, which are usually (but not necessarily) continuous, by using probability scores as the predicted values of the dependent variable.Thus, it treats the same set of problems as does probit regression AU90: The URL http://en.wikipedia.org/wiki/Probit_regression has been redirected to https://en.wikipedia.org/wiki/Probit_regression. Please verify the URL. using similar techniques; the first assumes a logistic function AU91: The URL http://en.wikipedia.org/wiki/Logistic_function has been redirected to https://en.wikipedia.org/wiki/Logistic_function. Please verify the URL. and the second a standard normal distribution AU92: The URL http://en.wikipedia.org/wiki/Normal_distribution has been redirected to https://en.wikipedia.org/wiki/Normal_distribution. Please verify the URL. function. Logistic regression can be seen as a special case of generalized linear model AU93: The URL http://en.wikipedia.org/wiki/Generalized_linear_model has been redirected to https://en.wikipedia.org/wiki/Generalized_linear_model. Please verify the URL. and thus analogous to linear regression AU94: The URL http://en.wikipedia.org/wiki/Linear_regression has been redirected to https://en.wikipedia.org/wiki/Linear_regression. Please verify the URL. . The model of logistic regression, however, is based on quite different assumptions (about the relationship between dependent and independent variables) from those of linear regression. In particular the key differences of these two models can be seen in the following two features of logistic regression. First, the conditional mean p(y/x) follows a Bernoulli distribution AU95: The URL http://en.wikipedia.org/wiki/Bernoulli_distribution has been redirected to https://en.wikipedia.org/wiki/Bernoulli_distribution. Please verify the URL. rather than a Gaussian distribution AU96: The URL http://en.wikipedia.org/wiki/Gaussian_distribution has been redirected to https://en.wikipedia.org/wiki/Gaussian_distribution. Please verify the URL. , because logistic regression is a classifier. Second, the linear combination of the inputs is restricted to [0,1] through the logistic distribution function AU97: The URL http://en.wikipedia.org/wiki/Logistic_function has been redirected to https://en.wikipedia.org/wiki/Logistic_function. Please verify the URL. because logistic regression predicts the probability of the instance being positive.

Probit Model: In statistics AU98: The URL http://en.wikipedia.org/wiki/Statistics has been redirected to https://en.wikipedia.org/wiki/Statistics. Please verify the URL. , a probit model is a type of regression AU99: The URL http://en.wikipedia.org/wiki/Regression_analysis has been redirected to https://en.wikipedia.org/wiki/Regression_analysis. Please verify the URL. where the dependent variable AU100: The URL http://en.wikipedia.org/wiki/Dependent_variable has been redirected to https://en.wikipedia.org/wiki/Dependent_variable. Please verify the URL. can only take two values, for example married or not married. The name is from probability + unit . The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, if estimated probabilities greater than 1/2 are treated as classifying an observation into a predicted category, the probit model is a type of binary classification AU101: The URL http://en.wikipedia.org/wiki/Binary_classification has been redirected to https://en.wikipedia.org/wiki/Binary_classification. Please verify the URL. model. A probit AU102: The URL http://en.wikipedia.org/wiki/Probit has been redirected to https://en.wikipedia.org/wiki/Probit. Please verify the URL. model is a popular specification for an ordinal or a binary response model AU103: The URL http://en.wikipedia.org/wiki/Binomial_regression has been redirected to https://en.wikipedia.org/wiki/Binomial_regression. Please verify the URL. . As such it treats the same set of problems as does logistic regression AU104: The URL http://en.wikipedia.org/wiki/Logistic_regression has been redirected to https://en.wikipedia.org/wiki/Logistic_regression. Please verify the URL. using similar techniques. The probit model, which employs a probit AU105: The URL http://en.wikipedia.org/wiki/Probit has been redirected to https://en.wikipedia.org/wiki/Probit. Please verify the URL. link function AU106: The URL http://en.wikipedia.org/wiki/Link_function has been redirected to https://en.wikipedia.org/wiki/Link_function. Please verify the URL. , is most often estimated using the standard maximum likelihood AU107: The URL http://en.wikipedia.org/wiki/Maximum_likelihood_estimation has been redirected to https://en.wikipedia.org/wiki/Maximum_likelihood_estimation. Please verify the URL. procedure, such an estimation being called a probit regression. Probit models were introduced by Chester Bliss AU108: The URL http://en.wikipedia.org/wiki/Chester_Ittner_Bliss has been redirected to https://en.wikipedia.org/wiki/Chester_Ittner_Bliss. Please verify the URL. in 1934; a fast method for computing maximum likelihood AU109: The URL http://en.wikipedia.org/wiki/Maximum_likelihood has been redirected to https://en.wikipedia.org/wiki/Maximum_likelihood. Please verify the URL. estimates for them was proposed by Ronald Fisher AU110: The URL http://en.wikipedia.org/wiki/Ronald_Fisher has been redirected to https://en.wikipedia.org/wiki/Ronald_Fisher. Please verify the URL. as an appendix to Bliss' work in 1935.

Financial Market: A financial market is a market in which people and entities can trade financial securities, commodities, and other fungible items of value at low transaction costs and at prices that reflect supply and demand. Securities include stocks and bonds, and commodities include precious metals or agricultural goods.

2001 Turkish Economic Crisis: Throughout the 1980s and 1990s, Turkey relied heavily on foreign investment AU64: The URL http://en.wikipedia.org/wiki/Foreign_direct_investment has been redirected to https://en.wikipedia.org/wiki/Foreign_direct_investment. Please verify the URL. for economic growth AU65: The URL http://en.wikipedia.org/wiki/Economic_growth has been redirected to https://en.wikipedia.org/wiki/Economic_growth. Please verify the URL. , with trade above 40% of GNP. The Turkish government AU66: The URL http://en.wikipedia.org/wiki/Politics_of_Turkey has been redirected to https://en.wikipedia.org/wiki/Politics_of_Turkey. Please verify the URL. and banking systems lacked the financial means to support meaningful economic growth. The government was already running enormous budget deficits AU67: The URL http://en.wikipedia.org/wiki/Budget_deficit has been redirected to https://en.wikipedia.org/wiki/Budget_deficit. Please verify the URL. , and one of the ways it managed to sustain these was by selling huge quantities of high-interest bonds to Turkish banks. Continuing inflation (likely a result of the enormous flow of foreign capital into Turkey) meant that the government could avoid defaulting on the bonds in the short term. As a consequence, Turkish banks came to rely on these high-yield bonds AU68: The URL http://en.wikipedia.org/wiki/High-yield_debt has been redirected to https://en.wikipedia.org/wiki/High-yield_debt. Please verify the URL. as a primary investment. On February 19, 2001, Prime Minister Ecevit emerged from a meeting with President Sezer saying, “This is a serious crisis.” This underscored financial and political instability and led to further panic in the markets. Stocks plummeted and the interest rate AU69: The URL http://en.wikipedia.org/wiki/Interest_rate has been redirected to https://en.wikipedia.org/wiki/Interest_rate. Please verify the URL. reached 3,000%. Large quantities of Turkish lira AU70: The URL http://en.wikipedia.org/wiki/Turkish_lira has been redirected to https://en.wikipedia.org/wiki/Turkish_lira. Please verify the URL. were exchanged for U.S. dollars AU71: The URL http://en.wikipedia.org/wiki/United_States_dollar has been redirected to https://en.wikipedia.org/wiki/United_States_dollar. Please verify the URL. or euro, causing the Turkish central bank AU72: The URL http://en.wikipedia.org/wiki/Central_Bank_of_the_Republic_of_Turkey has been redirected to https://en.wikipedia.org/wiki/Central_Bank_of_the_Republic_of_Turkey. Please verify the URL. to lose $5 billion of its reserves. The crash triggered even more economic turmoil. In the first eight months of 2001, 14,875 jobs were lost, the dollar rose to 1,500,000 liras, and income inequality AU73: The URL http://en.wikipedia.org/wiki/Income_inequality has been redirected to https://en.wikipedia.org/wiki/Income_inequality. Please verify the URL. had risen from its already high level.

Banking Crisis: When a bank suffers a sudden rush of withdrawals by depositors, this is called a bank run . Since banks lend out most of the cash they receive in deposits (see fractio nal-reserve banking AU74: The URL http://en.wikipedia.org/wiki/Fractional-reserve_banking has been redirected to https://en.wikipedia.org/wiki/Fractional-reserve_banking. Please verify the URL. ), it is difficult for them to quickly pay back all deposits if these are suddenly demanded, so a run renders the bank insolvent, causing customers to lose their deposits, to the extent that they are not covered by deposit insurance. An event in which bank runs are widespread is called a systemic banking crisis or banking panic . Examples of bank runs include the run on the Bank of the United States in 1931 AU75: Anchored Object 2 and the run on Norther n Rock AU76: The URL http://en.wikipedia.org/wiki/Northern_Rock has been redirected to https://en.wikipedia.org/wiki/Northern_Rock. Please verify the URL. in 2007. Banking crises generally occur after periods of risky lending and resulting loan defaults.

Economic Crisis: The concept of crisis, which is widely used in the literature of various disciplines and in daily language, has its etymological roots in Greek “krisis” (???s?). The concept of economical crisis first entered the social sciences literature in 1960’s. Economic crisis could be defined as a period of difficulty, dismay or an emergency in the life of a country, a society or a corporation, or in relations of several countries. In other words, an economic crisis is an unforeseen set of developments creating results which would affect states in the macro level and corporations in the micro level. According to another definition, economic crisis could be expressed as a situation that develops unexpectedly in the operation of the financial system or its sub-components and affects the operation of the system in a significantly negative manner. Economic crises experienced in national economies are usually a product of negative fallout in the economical and political cycles and structures. But it could be stated that economic crises are a general outcome of macro economical instability.

Financial Crisis: The term financial crisis is applied broadly to a variety of situations in which some financial assets suddenly lose a large part of their nominal value. In the 19th and early 20th centuries, many financial crises were associated with banking panics AU79: Anchored Object 1 and manyrecessions coincided with these panics. Other situations that are often called financial crises include stock market crashes and the bursting of other financial bubbles, currency crises, and sovereign defaults. Financial crises directly result in a loss of paper wealth but do not necessarily result in changes in the real economy. Many economists have offered theories about how financial crises develop and how they could be prevented. There is no consensus, however, and financial crises continue to occur from time to time.

Currency Crisis: There is no widely accepted definition of a currency crisis, which is normally considered as part of a financial crisis. Kaminsky et al. (1998) AU77: The in-text citation "Kaminsky et al. (1998)" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. , for instance, define currency crises as when a weighted average of monthly percentage depreciations in the exchange rate and monthly percentage declines in exchange reserves exceeds its mean by more than three standard deviations. Frankel and Rose (1996) AU78: The in-text citation "Frankel and Rose (1996)" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. define a currency crisis as a nominal depreciation of a currency of at least 25% but it is also defined at least 10% increase in the rate of depreciation. In general, a currency crisis can be defined as a situation when the participants in an exchange market come to recognize that a pegged exchange rate is about to fail, causing speculation against the peg that hastens the failure and forces a devaluation or appreciation.

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