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Alexander Taser

April 25, 2014

Updated: June 2, 2014

Department of Economics, San Francisco State University

Predicting Currency Crises in Emerging Economies: The Case of Turkey

Abstract

This research paper develops an early-warning system (EWS) that estimates the

likelihood of a currency crisis in emerging markets, with an empirical focus on Turkey. It

uses speculative pressure index to determine the threshold between calms periods and

periods of currency crises, and then applies a logistic (logit) probability regression model

to evaluate the effects of relevant macroeconomic variables on the probability of a

currency crisis. This paper finds that the monthly relative change in the international

reserves; the yearly relative change in the consumer price index; and the growth of

exports all played a significant role in the speculative episodes experienced by Turkey

from 1980 to 2013.

Introduction

A currency crisis is characterized by a speculative attack (or heavy selling

pressure) on a country’s currency, which either results in a sharp depreciation in that

currency, or forces the monetary authority to sell-off international reserves and/or to raise

domestic interest rates. For an economy under a fixed exchange rate regime, a speculative

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attack may force the country to abandon the peg, or devalue its currency to its appropriate

market value. Underlying weak macroeconomic fundamentals, coupled with a loss in

investor confidence, are the common causes of most speculative attacks of such

magnitude.

Currency crises have measurable costs for both the country in which it occurs in,

and for the global economy. It is any central bank’s objective to avoid such crises by

implementing preemptive measures when a future currency crisis may be detected. Can

we develop an accurate EWS that forecasts the event of a currency crisis? If so, we could

use this model to make preemptive policy measures to deter from future economic

disaster.

Literature Survey

Paul Krugman (1979) was the first to give theoretical attention to currency crises

in his classic 1979 seminal paper. The model is concerned with a country that gradually

losses international reserves at its current fixed exchange rate. At some point during this

country’s gradual decline in reserves, well before the reserves are exhausted, the county

experiences a sudden speculative attack that rapidly eliminates the last of its foreign

reserves. Krugman shows, using a mathematically based dynamic model, that these

currency crises are the “natural outcome of maximizing behavior by investors”. Under

such precarious economic conditions, investors will change the composition of their

portfolios, reducing the proportion of the domestic currency and increasing the proportion

of foreign currency, and as a result reducing overall losses. When the monetary

authority’s willingness to defend the pegged exchange rate is uncertain, there can be a

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series of speculative attacks, in which there are large capital reversals until the issue is

resolved.

Kaminsky, Lizondo, and Reinhart (1998) were the first to propose an early-

warning signals approach for predicting the event of a currency crisis, using data from

both developing and industrial economies. The authors define a currency crisis as a

situation in which “an attack on the currency leads to a sharp depreciation of the

currency, a large decline in international reserves, or a combination of the two”. An

indicator is said to issue a signal when it departs from its mean beyond a given threshold.

In their empirical model, a currency crisis is identified when the index rises above its

mean by three standard deviations. The empirical analysis includes 15 indicators. Of the

15, the indicators that prove to be useful in anticipating crises include: international

reserves (in US dollars); the real exchange rate; domestic credit; credit to the public

sector; and domestic inflation. Other supportive indicators include the trade balance,

export performance, money growth, real GDP growth, and the fiscal deficit. Some

indicators issue signals when there is no instance of a crisis (known as a bad signal,

“noise”, or type II error), while other indicators failed to issue a warning preceding a

crisis (type I error). A good signal is rated by its accuracy and its tendency not to issue

type I, and type II errors.

Berg and Pattillo (1999) study the validity of three different models developed

before 1997 that estimate the probability of a currency crisis. The first model, developed

by Kaminsky, Lizondo, and Reinhart (1998), is discussed above. The Frankel and Rose

(1996) probit model estimates the probability of a currency crisis using annual data for

more than 100 developing countries from 1971 to 1992. The use of annual data restricts

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the applicability of the model as an EWS but allows the analysis of certain variables that

high frequency data cannot provide. FR define a currency crisis as a “nominal exchange

rate depreciation of at least 25 percent that also exceeds the previous year’s change in the

exchange rate by at least 10 percent”, which does not include speculative attacks

successfully warded off by the sale of foreign reserves or hikes in the interest rate. Berg

and Pattillo conclude that the overall forecasts of the FR model are not that successful,

having a correlation of predicted probabilities and actual values of nominal exchange rate

depreciation of 33%. Also overviewed by BP, The Sachs, Tornell, and Velasco (1996)

cross-country regressions model analyze the impact of Mexico’s financial crisis of

December 1994 on other emerging markets in 1995. STV define a crisis index as the

weighted sum of the percentage decrease in reserves and the percentage depreciation of

the exchange rate. They found that countries had more severe attacks when their banking

systems were weak (measured as the growth in credit to the private sector ratio).

Karakis and Moschos (2004) investigate the role of macroeconomic fundamentals

in the speculative attacks experienced by the two emerging economies of the Czech

Republic and Poland during the 1990s by developing a probability (probit) regression

model. They first present a theoretical model developed by Sachs (1996) that shows that

devaluation occurs when net capital outflow exceeds the foreign reserves, in which case

the government chooses a new nominal exchange rate in order to achieve a target real

exchange rate. In their empirical analysis, KM identify a currency crisis as an episode in

which the value of the foreign exchange rate index exceeds a certain threshold. The

probit regression, which measures the probability of a speculative attack on both the

Czech Republic and Poland, includes the following independent variables in its analysis:

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the growth rate of the reserve adequacy ratio; the growth rate of the domestic credit; and

the growth rate of the real exchange rate. The R-squared statistic is high for both

countries, which implies that a significant proportion of the observations is correctly

predicted by the model. Their empirical findings suggest that a “rise in the growth rate of

the money supply relative to the growth rate of foreign exchange reserves is associated

with a rising probability of a currency crisis.” Thus, a policy aimed at increasing the

growth of the money supply relative to growth rate of foreign reserves and reducing the

real overvaluation of the domestic currency will reduce the likelihood of a currency

crisis.

Candelon, Dumitrescu, and Hurlin (2012) provide a statistical evaluation toolbox

for an EWS of any financial crisis. Their comprehensive empirical analysis provides

three different methods to quantify how well an EWS discriminates between calm and

crisis periods by identifying an optimal cut-off. The first method for defining an optimal

cut-off minimizes the “Noise-to-Signal” ratio used by KLR (1998). The second is the

credit-scoring approach, which is the threshold that minimizes the absolute value of the

difference between sensitivity (proportion of periods correctly identified by EWS) and

specificity (proportion of calm periods correctly identified). The third, and most accurate,

the “accuracy measure” maximizes the sum of the sensitivity and specificity minus 1.

BDH then propose evaluation techniques for EWS, and employs these techniques in real

econometric application, using a sample of 12 emerging countries for the period January

1980 to December 2010. They then compare the forecasting abilities of the various

methods. BHD conclude that the use of an optimal cut-off leads on average to a correct

identification of at least 2/3 of the crisis and calm periods. BHD’s paper provides

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professional guidance for any researcher attempting to estimate the probabilities of

financial crises.

Theoretical Argument

A currency crisis occurs when international investors attack the domestic currency

in speculation of a depreciation. For an economy under a fixed exchange rate regime, a

speculative attack may force the county to abandon the current peg to allow the currency

to adjust to its intrinsic market value before losing a substantial portion of its

international reserves. In special cases, however, a country may negotiate an emergency

loan (i.e. from the IMF), or call and a secondary reserve (i.e. gold) in an attempt to

defend the peg, although most efforts to defend an overvalued pegged regime under

heavy speculative pressure often fail1.

Currency crises may also exhibit self-fulfilling features. In a situation where a

country has an “intermediate” level of reserves, a coordination problem may occur

among investors, which gives rise to multiple currency-market equilibria2. Underlying

macroeconomic fundamentals are, indeed, important to the outcome, for they determine

the possible range of the equilibria. In the case of an economy with intermediate reserves,

the equilibria have a self-fulfilling element – if the currency is attacked, the country

experiences a crisis, but no crisis occurs otherwise.

Brief History of Turkey’s Currency Crises

In 1993 and early 1994, requirements for public borrowing combined with policy

1 See Krugman (1979). 2 See Obstfeld (1996)’s game theoretic model.

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errors in financing the public deficit led to a series of speculative attacks against the

Turkish Lira, which consequently led to a currency crisis in Turkey. As a result, output

fell 6 percent, inflation rose to 3 digit extremes, the Central Bank of the Republic of

Turkey lost more than half of its reserves, and the exchange rate against the US dollar

depreciated by more than half in the first three months of 19943. A few years later, a high

current account deficit, a real appreciation of the lira, as well fragility in the banking

system led to another currency crisis in 2001. During this event, Turkey had abandoned

its pegged exchange rate to adopt a floating regime. The Central Bank of Turkey declared

that the exchange rate would be determined by market dynamics, although it was not

entirely, being governed occasionally by the monetary authority.

Empirical Structure

This paper constructs an early-warning system (EWS) that forecasts the event of a

currency crisis in Turkey4. The empirical analysis investigates the macroeconomic

indicators affecting the probability of a currency crisis by using a binary logistic (or logit)

model that assumes values of 0s for calm periods and 1s for periods of crisis. Let us

consider the logit EWS, and let yt represent the binary crisis variable for Turkey at time,

t ∈ 1,2,...,n{ } . The formal crisis logit probability is defined as:

P yt =1( ) =exp u+

!βT !xt( )

1+ exp u+!βT !xt( )

3 See Celason (1998). 4 It would have been more inclusive to analyze a group of countries, however high frequency international data is limited on emerging economies. Turkey is an exception with available monthly international data.

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where !xt denotes a vector containing macroeconomic indicators, !β represents the vector

of unknown slope parameters, and u is a constant.

In general EWSs must have a threshold that demarcates a calm period from a

crisis. This model identifies a currency crisis by the modified Kaminsky, Lizondo and

Reinhart (KLRm) approach5, as a situation in which the change in the modified

speculative pressure index exceeds two standard deviations from its mean. The KLRm

modified speculative index is defined as the following:

KLRmt =Δetet

−σ e

σ r

Δrtrt+σ e

σ ir

Δirt , for nt ,...,3,2,1=

where et denotes the exchange rate at time t (that is, Turkish Lira per US dollar), rt

denotes the international reserves at time t , while irt is the interest rate at time t .

Meanwhile the standard deviation, σ X , is actually the standard deviation of the relative

changes in the variables, σ ΔXt Xt , where X denotes each variable separately (which

includes the exchange rate and international reserves) with ΔXt = Xt − Xt−6 . However, it is

important to note that just for the interest rate, σ ir is the standard deviation of the

absolutes change in interest rates.

Now we define the estimated binary currency crisis variable, which will be the

dependent variable of the logit probability model:

yt =1, if KLRmt > 2σ KLRm +µKLRm

0, Otherwise

!"#

$#

5 Developed by Lestano, Jacobs, and Kuper (2004).

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The estimated currency crisis of the sample data, yt , takes a value of 1 when theKLRmt

speculative pressure index exceeds two standard deviations from the mean (indicating a

crisis), and takes a value of 0 otherwise

The Data and Estimation

The data used in this empirical model was obtained from the Federal Reserve

Bank of St. Louis, since their database contains high frequency international data that is

generally difficult to access elsewhere. Because of limitations on data accessibility, I had

to reduce the number of emerging economies to just one, Turkey. I collected monthly

data from January 1, 1980 to November 1, 2013, which is 407 months in total.

In regards to its accuracy, the KLRm speculative pressure index threshold

correctly measures 2 periods of currency crises in Turkey, setting off 6 signals in

consecutive months from January 1994, and just 1 signal in 2001. There were also 2

signals set off in September and October of 1982, and 3 signals in February, March and

April of 1984, when emerging markets experienced dramatic volatility. That means the

sample data show 12 recorded currency crises by the KLRm measure, 7 of which

currency crises officially occurred.

The table below displays the following data that was used to construct the logit

probability model. The estimated binary crisis variable, yt , shows the percentage of calm

periods and the percentage of crises that occurred during the sample time interval. The

growth rate of each quantitative variable was obtained by taking the natural logarithm of

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those variables, and both the real interest rate and the real exchange rate were computed

by dividing their nominal quantities by the Consumer Price Index (CPI) in Turkey.

Categorical Variable Percentage of Sample

Crisis 2.948%

yt

Calm Period 97.052%

Quantitative Variables Mean

Standard Deviation Minimum Maximum

Monthly Relative Change in International Reserves 1.68% 9.10% -31.36% 39.77%

Yearly Relative Change in International Reserves 45.13% 31.01% 3.99% 125.89%

Growth of International Reserves 23.08% 1.59% 20.20% 25.46%

Growth of M2 to International Reserves Ratio -1.85% 3.10% -7.42% 2.16%

Yearly Relative Change in the CPI 45.13% 31.01% 3.99% 125.89%

The Interest Rate (Discount Rate) 41.54% 16.71% 9.50% 79.00%

The Exchange Rate (Lira to Dollar) 0.62 0.71 0.000 2.02

The Real Interest Rate 2026.99% 3735.87% 0.07% 14898%

The Real Exchange Rate 0.03 0.01 0.01 0.08

Growth of Imports 21.92% 1.11% 19.72% 23.87%

Growth of Exports 21.46% 1.13% 18.79% 23.35%

Monthly Relative Change in the Current Account Balance 2.30% 148.54% -1928.5% 499.68%

Yearly Relative Change in the Current Account Balance 48.36% 1940.97% -11832% 30393%

Empirical Model and Interpretation

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The table below displays the Logistic coefficients, the average marginal effects, and

the average marginal elasticities, and all their corresponding levels of statistical

significance.

Number of Observations 407 Likelihood Ratio Chi-Square (10) 35.82 Probability > Chi-Square 0.0001 Pseudo R-Squared 0.331

Independent Variables Logistic Coefficient dy/dx ey/ex

Monthly Relative Change in International Reserves -0.0752139** -0.0017855** -0.13516**

Growth of International Reserves -1.238524 -0.0294007 -27.795

Growth of M2 to International Reserves Ratio 0.9027082 0.0214289 -1.571346

Yearly Relative Change in the CPI 0.0516588*** 0.0012263*** 2.232892***

The Real Interest Rate 0.0003626* 0.00000861* 0.6919167*

The Real Exchange Rate 70.74667 1.679421 2.367386

Growth of Imports -4.407602* -0.1046299* -93.89269*

Growth of Exports 4.915617** 0.1166894** 102.4863**

Monthly Relative Change in the Current Account Balance 0.0007527 0.0000179 0.0017138

Yearly Relative Change in the Current Account Balance -0.0003174 0.00000754 -0.0170003

Constant 10.47069 0 0

Note: The asterisks *, **, and *** denote statistical significance at 90, 95, and 99 percent confidence level, respectively.

Based on the empirical results above, the yearly relative change in the consumer

price index seems to be the most statistically significant variable of our above logit

probability model. A 10% increase in the yearly relative change in the CPI, leads to an

estimated 1.23% increase in the probability of a currency crisis on average, holding all

other variables constant.

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Also consistent with macroeconomic theory is the sign on the average marginal

effect of the monthly relative change in reserves – a 10% loss in international reserves

increases the probability of a currency crisis by a predicted 1.79%.

It is peculiar, however, that the signs and significances of the growth of imports

and growth of exports are contrary to macroeconomic principles. In most cases, a high

export growth relative to import growth yields a health and stable macroeconomy, but the

above results suggest that a 1% increase in export growth increases the probability of a

currency crisis by 11.68%; and that a 1% increase in imports leads to a 10.46% decrease

in the probability of a currency crisis on average. A discerning economist may postulate

that a growing current account surplus indicates a depreciating currency.

Conclusion

This research paper develops an EWS that attempts to accurately estimate the

probability of a currency crisis in emerging economies, focusing on the specific case of

Turkey. Although some significant empirical results were obtained using a logit

probability model, more serious statistical work should be completed for applicable

implementation. Most of the signs on the average marginal effects of the economic

indicators were expected from macroeconomic theory, however both the growth of

exports and the growth of imports gave rise to two peculiar, significant results, which

may need further investigation. One feasible explanation could be that a growing current

account surplus is resulting from a depreciating currency.

But we can for now conclude that the monthly relative change in the international

reserves; the yearly relative change in the consumer price index; and the growth of

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exports all played an important role in the speculative episodes experienced by Turkey

during the timeframe of January 1980 to November 20136.

6 At 5% significance.

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Reference list:

Berg, Andrew, and Catherine Pattillo. "Are Currency Crises Predictable? A Test." IMF Staff Papers 46.2 (1999): 107-138. Celasun, Oya. "The 1994 Currency Crisis in Turkey." The World Bank Research (1999) Candelon, Bertrand, Elena-Ivona Dumitrescu, and Christophe Hurlin. "How To Evaluate An Early-Warning System: Toward A Unified Statistical Framework For Assessing Financial Crises Forecasting Methods." IMF Economic Review 60.1 (2012): 75-113. Kaminsky, Graciela, Saul Lizondo, and Carmen M. Reinhart. "Leading Indicators Of Currency Crises." International Monetary Fund Staff Papers 45.1 (1998): 1-48. Karfakis, Costas, and Demetrios Moschos. "Predicting Currency Crises: Evidence From Two Transition Economies." Emerging Markets Finance And Trade 40.1 (2004): 95-103. Krugman, Paul. "A Model of Balance-of-Payment Crises." Journal of Money, Credit and Banking 11.3 (1979): 311-25. Lestano, Jacobs, P.A.M. Jan, and Gerard H. Kuper, 2003, “Indicators of Financial Crises Do Work! An Early-Warning System for Six Asian Countries,” Working Paper. Obstfeld, Maurice. "Models of currency crises with self-fulfilling features." European Economic Review (1996) Özatay, Fatih. "TURKEY’S 2000-2001 FINANCIAL CRISIS AND THE CENTRAL BANK’S POLICY IN THE AFTERMATH OF THE CRISIS." Bank of Albania in the Second Decade of Transition (2002)