currency crises in emerging markets: the case of post-liberalization turkey

The Developing Economies

, XLVI-4 (December 2008): 386–427

© 2008 The Author doi: 10.1111/j.1746-1049.2008.00071.xJournal compilation © 2008 Institute of Developing Economies

Blackwell Publishing LtdOxford, UKDEVEDeveloping Economies0012-15331746-10492008 The AuthorsJournal compilation © 2008 Institute of Developing EconomiesXXXOriginal Article

currency crises: the case of turkeyTHE DEVELOPING ECONOMIESD

CURRENCY CRISES IN EMERGING MARKETS: THE CASE OF POST-LIBERALIZATION TURKEY

M

ete

FERIDUN

Department of Economics, Loughborough University, Leicestershire, UK

First version received March 2007; final version accepted October 2008

This article investigates the determinants of currency crises in Turkey. It analyzes the twomajor currency crises of 1994 and 2000–2001 in the light of the existing theoretical models.The present study uses logit, probit, and limited dependent models to explain the cur-rency crises in the post–capital account liberalization era. The results obtained from thethree approaches are generally consistent and the coefficients obtained for the explana-tory variables generally have the same sign. The findings suggest that the currency crisesin Turkey are associated with global liquidity conditions, fiscal imbalances, capital out-flows, and banking sector weaknesses.

Keywords

: Currency crises; Logit, probit, limited dependent models; Turkey

JEL classification

: F31, F37

I. INTRODUCTION

F

inancial

liberalization policies of the 1980s have exposed emerging economiesto speculative short-term capital movements and rendered them vulnerable tocurrency crises. In Turkey, the first phase of the liberalization process was

initiated in the early 1980s with an International Monetary Fund (IMF)–backedstructural adjustment reform program that was put into effect on January 24, 1980to restore the economy, which had suffered from a major debt crisis from 1977 to1980. The purpose of the program was to liberalize the economy, to create a market-based system, to reduce inflation, and to increase the efficiency of the banking sector.With the adoption of the program, a series of stability measures were adopted toswitch the national economic policy from import-substituting industrialization toan export-led growth strategy. The import regime was liberalized and incentives

The author is indebted to David T. Llewellyn, Eric J. Pentecost, Hakan Berument, Sorin Tuluca, andthe two anonymous referees of this article for their helpful suggestions on an earlier draft of this article.The author would also like to thank the participants at the Oxford Business and Economics Conferenceheld at the University of Oxford and the Departmental Seminar Series held at Loughborough Universityfor their insightful comments.

currency crises: the case of turkey

387

© 2008 The AuthorJournal compilation © 2008 Institute of Developing Economies

were initiated to promote exports. On the monetary policy front, interest ratecontrols were eliminated, banks were allowed to issue certificates of deposits. Inaddition, a supply and demand system in foreign exchange market was put intopractice, and the Turkish lira was left to float in a controlled monetary environment.

The second phase of the financial liberalization process was completed in the late1980s when all the restrictions on capital movements were lifted, leading to a periodcharacterized by financial openness and subsequent currency crises. With the liber-alization of the capital account in August 1989, the economy experienced a massiveinflow of short-term capital and the threat of capital reversals became a dominantmotive in policymaking, which necessitated a firm commitment to high interest rates.

The liberalization of the capital account resulted in the subsequent appreciationof the Turkish lira by 22% by the end of 1989. Thereafter, the economy witnessedtwo major currency crises in 1994 and 2000–2001. The present article aims to inves-tigate the root causes of currency crises in Turkey in the post–capital liberalizationperiod using logit, probit, and linear probability models. Only by knowing thecauses of past crises can policymakers take measures to mitigate or even preventfuture crises. Accordingly, identification of the leading indicators of currency crisesis a research challenge of substantial importance. In this respect, the present analysiscould yield lessons not only for Turkish policymakers but also for those in otherliberalizing economies.

The rest of the article is structured as follows: The next section will review theexisting literature on Turkish currency crises. Section III will provide an analysis ofthe currency crises of 1994 and 2000–2001 in the light of the existing theoreticalmodels of currency crises. Section IV will introduce the data and explain the meth-odology. Section V will present the empirical results obtained from the empiricalanalysis. Section VII will investigate the impact of global liquidity conditions on thelikelihood of crises. Section VI will point out the conclusions and the policy impli-cations that emerge from the study.

II. LITERATURE REVIEW

The existing literature on the currency crises in Turkey is not very rich. Followingthe crisis of 1994, a stream of empirical research emerged to identify the determi-nants of currency crises. The earliest example in this genre is Ucer, Rijckeghem, andYolalan (1998), who examine the currency crisis of 1994 using signals approachbased on quarterly data. The authors determine that the crisis could be explained bythe fluctuations in short-term foreign debt, exports to imports ratio, short-termadvances to the Treasury, and monetary expansion.

Kibritçio

©

lu, Köse, and U

©

ur (1998) also analyze the crisis of 1994 using theleading indicators approach based on monthly data. The authors test a broad setof indicators and identified effective real exchange rate, exports to imports ratio,

388

the developing economies


foreign trade balance, current account balance and short-term capital movements asthe leading indicators of their crisis index. In an application of the Markov regimeswitching model of exchange rate movements to Turkish economy, Mariano

et al

.(2004) test both monthly and weekly models and find the real exchange rate, foreignexchange reserves, and domestic credit to deposit ratio to be the most importantdeterminants of financial vulnerability.

Parlaktuna (2005) run OLS regression for the period 1993–2004 and find strongevidence of a negative relation between domestic credit and exchange market pres-sure. Boduroglu and Erenay (2007) design a scalar composite leading indicator thatpredicts a financial crisis in Turkey approximately six months in advance, and Özlaleand Metin-Özcan (2007) analyze currency crises in Turkey in a nonlinear state spaceframework and find that the overvalued domestic currency, the current account deficitand the increase in the default risk increased the likelihood of having an economiccrisis in the economy.

In summary, the existing empirical studies in the literature differ vastly in choiceof methodology, variables, crisis definition, as well as sample period. These studieshave yielded mixed results and a general disagreement remains on the causes ofcurrency crises. An obvious drawback of the existing articles is that they ignore thetime-series properties of the variables. However, if the variables are nonstationary,the results may not be reliable. The present article addresses this shortcoming of theexisting studies by using several unit root tests.

III. ANALYSIS OF THE CRISES OF 1994 AND 2000–2001

This section analyzes the currency crises of 1994 and 2000–2001 in Turkey in thelight of the existing currency crisis theories. The first-generation models of currencycrises explain currency crises through speculative attacks that take place due toinconsistencies between an exchange rate peg and domestic economic fundamen-tals. In particular, they hypothesize a government that runs a persistent fiscal deficit,which is financed through domestic credit expansion. According to these models, thecollapse of the fixed exchange rate system is inevitable because the monetaryauthorities must eventually run out of reserves. These models consider lax fiscalpolicies as the source of domestic credit expansion. In the case of Turkey, lack offiscal adjustment in the post–capital account liberalization period resulted in persistentfiscal imbalances. Before the crisis of 1994, the Treasury relied heavily on cashadvances from the Central Bank (Celasun 1998). At the same time, public accountscontinuously deteriorated and the economy never achieved a period of sustainedfiscal austerity. As can be seen in Figure 1, the primary balance was in deficit in thepre-1994 period. The budget balance, in contrast, was in deficit throughout allthe years. As can be seen in Figure 2, there was a large deficit in 1993, just beforethe crisis of 1994. Together with the persistent primary deficits, this suggests that the


389


crisis of 1994 carried the elements of first-generation type crises as far as the fiscaldeficits are concerned.

The primary deficits in the pre-1994 period were caused by the surge in publicexpenditures stemming from the total wage bill of the government, the generousagricultural support policies, the worsening performance of the state-ownedeconomic enterprises, the increased cost of military operations in the southeastern

Fig. 1. Primary Balance (% of GNP)

Fig. 2. Budget Balance (% of GNP)

390



region of the country,

1

persistent problems in the taxation system and increasedinterest payments on existing debt after 1992. As can be seen in Figure 3, in the sameperiod, domestic debt stock rose sharply from 6.8% of gross national product (GNP)in 1989 to 12.8% of GNP in 1993 due to high primary deficits of the public sector.To service their debt, government was engaging in Ponzi financing, where new debtsequaled 50% of the existing stock of debt over the decade (see Boratav, Yeldan, andKöse 2001; Voyvoda and Yeldan 2002). Moreover, real interest rates on domesticborrowing were increasing constantly, which constituted a source of further deteri-oration in public balances. In the post-1994 period, in contrast, the unsustainablenature of domestic debt following the 1994 crisis forced the policymakers to keepthe primary balance in surplus. To strengthen the consolidated public stance perma-nently and to create a sustainable debt structure, the disinflation programs put limitson the primary balance of the public sector. Hence, as plotted in Figure 1, there wasa consistently increasing level of primary surplus following 1994. In this respect, thetwin crises of 2000–2001 lacked the elements of the first-generation models.

In addition to this, the public-sector borrowing requirement (PSBR) was con-sistently high prior to the crisis of 1994. Figure 4 plots the PSBR as a share of GNP,which measures the true stance of fiscal policy and the extent of fiscal adjustmentneeded. As explained above, the major cause of the high PSBR was the Ponzi financ-ing policy of the government, which was paying interest payments on its existingdebt by issuing new debt.

1

See Feridun and Sezgin (2008).

Fig. 3. Domestic Debt (% of GNP)


391


To assess the economy’s capacity to service its debt more accurately, Figures 5and 6 plot the interest payments on domestic debt as a share of GDP and the maturitystructure of domestic borrowing. The figures suggest that interest paymentsincreased in the post-1994 period, whereas the maturity of the outstanding debt wasof longer nature in this period. In particular, it is obvious from Figure 5 that thecontinuous increase in the public-sector borrowing requirement after 1995 can beattributed to the interest payments on domestic debt.

Fig. 4. Public Sector Borrowing Requirement (% of GNP)

Fig. 5. Interest Payments on Domestic Borrowing / GDP

392



The degree of monetary policy credibility is also an important factor determiningfiscal position. Conceptually, in the presence of primary deficits, the governmentmust either deplete its foreign exchange reserves, or borrow to finance the deficit.Because it is impractical for the government to borrow or deplete reserves indefi-nitely, in the absence of fiscal reforms, it must eventually finance the deficit by print-ing money to raise seigniorage revenue. Because printing money is inconsistent withkeeping the exchange rate fixed, first-generation models predict that the regime must

Fig. 6. Maturity of Domestic Debt

Fig. 7. M1 Growth


393


collapse. Figure 7 plots annual percentage M1 growth in Turkey. Excess liquidity in1993 is noticeable in line with what the first-generation models postulate. A betterproxy for monetary expansion, excess real M1 balances, is also plotted in Figure 8.This indicator also verifies that the supply of money in the economy was in excessof demand in 1993.

It is also important to see how the government financed the deficits. A key indi-cator suggested by the first-generation models is domestic credit expansion.Figure 9 plots the evolution of this indicator. Analyzing the structure of domestic

Fig. 8. Excess Real M1 Balances

Fig. 9. Domestic Credit / GNP

394



credit in the pre-1994 period, it can be concluded that domestic credit graduallyincreased until 1994. Figure 10 shows a clearer picture regarding the domestic creditpolicy of the Central Bank before the crisis of 1994. The Central Bank financed thedeficits through excessive creation of domestic credit in the form of cash advancesto the Treasury during this period. In particular, the sharp increase in the CentralBank’s cash advances to the Treasury in 1993 clearly indicates the extent of excessliquidity that was pumped in to the system immediately before the 1994 crisis. Thiswas mainly caused by the government’s attempt to decrease the cost of domesticborrowing by canceling Treasury auctions in 1993, which left the Treasury to relyheavily on central bank resources. As can be seen in Figure 10, the Treasury’s accessto cash advances from the Central Bank was cut off with the stabilization programof 1995, which was introduced to improve fiscal discipline.

Regarding the twin crises of 2000–2001, it is clear that the excess liquidity crea-tion through extension of the Central Bank advances to the Treasury stopped in thepost-1994 period, which suggests that the budget deficits were not financed by thecentral bank resources in this period. In addition, there was no significant growth inoverall domestic credit during this period. In fact, there was a decrease in domesticcredit in 1999. As argued by Özatay and Sak (2003) and Ozkan (2005), this showsthat the tight money control as specified by the stabilization program of 1994 wasstrictly adhered to. Therefore, another major element of the first-generation modelswas not present in the crisis of 2000–2001.

An interesting observation regarding the post-1994 period is that the public-sectorborrowing requirement was in fact healthier in 2000 compared to 1999. This ratio

Fig. 10. Central Bank Advances to Treasury (% of GNP)


395


had decreased with the discipline imposed on the public finances by the IMF-ledstabilization program, which was launched in 1999. The program succeeded inreversing this negative trend. Relative to 1999, there was a very significant increasein primary surplus in 2000 (see Figure 1). Consequently, the debt to GNP ratio andthe public-sector borrowing requirement also decreased. The fiscal performance of1999 seems to be the worst in the 1995–2000 period. It is therefore counterintuitivethat the crisis burst at the end of 2000 and not in 1999. Therefore, as far as the twincrises of 2000–2001 are concerned, these developments do not fit very well in thefirst-generation theory of currency crises. Overall, the evidence suggests that thecrisis of 1994 carried the elements of the first-generation models of currency crisesin the sense that deteriorated fiscal fundamentals and domestic credit expansionthrough cash advances from the Central Bank to the Treasury eventually led to thedepreciation of the Turkish lira.

However, although fiscal imbalances were not completely eliminated in the post-1994 period, the twin crises of 2000–2001 cannot be accurately explained by thefirst-generation models, primarily because most of the fundamentals were healthierin 2000 compared to their levels in 1999. The fact that the crisis burst at the end of2000 rather than 1999 brings to mind a self-fulfilling crisis. Furthermore, in the post-1994 period, deficits had been mainly financed by issuing domestic debt rather thanby monetization, which is at the core of such models. The vulnerability of the econ-omy in the absence of the elements of a first-generation type crisis and the improvingstance in macroeconomic fundamentals due to the stabilization program of 1999suggests that the twin crises of 2000–2001 could have been a result of second-generation type self-fulfilling expectations.

Second-generation models link currency crises to market expectations and arguethat speculative attacks can take place due to sudden shifts in market expectations,not necessarily coupled with any sizable worsening in the fundamentals. Accordingto these models, there can be more than one equilibrium, depending on the costs andbenefits of the devaluation for the government, and the economy can jump from oneequilibrium to another due to a sudden shift in expectations. Consequently, thetiming of the speculative attacks is not predictable. The existing published literaturesuggests a few variables that can be used in the context of second-generation models,such as inflation, output growth, competitiveness, interest rates, and unemployment.These variables are essentially used to quantify the cost of a peg to the government.For instance, a rise in interest rates required to maintain the peg could lower invest-ment and, consequently, lead to poor output performance. In the case of Turkey, thereal interest rates in the pre-1994 period were generally on a decreasing path,whereas they were constantly high in the post-1994 period (see Figure 11). In par-ticular, there was a growth in real interest rates in 1999 and 2000 compared to 1998.

Turning to the overall performance of the economy, the GNP growth was quitehigh in 1993, reducing the possibility of a second-generation type crisis that would

396



stem from a poor output performance. In 1999, in contrast, the economy was under-going a contraction, evident from the negative growth rate (see Figure 12). Thisepisode of recession can be attributed to the impact of the Russian crisis of 1998 anda devastating earthquake in 1999. Because of the poor performance of the economy,the government launched an IMF-backed stabilization program in December 1999.Consequently, the growth rate sharply turned positive in 2000. Therefore, it is inter-esting that the crisis still took place in 2000 rather than 1999, when the economy was

Fig. 11. Real Interest Rate

Fig. 12. Real GNP Growth


397


in a deep recession. Overall, there exists no evidence before the 1994 crisis thatcould justify a self-fulfilling speculative attack. However, the twin crises of 2000–2001 took place in the midst of an economic recovery period, which suggests thatthe confidence of the market agents was not very firm. Second-generation modelssuggest that the expectation of a devaluation is also likely to cause higher wagedemands and, therefore, lead to lower employment, which, in turn, increases thepressure to abandon the peg. In the case of Turkey, the unemployment rate was alsofollowing an increasing trend both before the 1993 and the 2000–2001 crises (seeFigure 13). This indicates that maintaining the peg was more costly for the govern-ments before the crises. In particular, in 1999, there was a jump in the unemploymentrate. This is no surprise as the economy was in a recession in the same year. Deteri-oration of output and employment was likely to alter the balance of perceived costsand benefits of the peg for the Central Bank in favor of abandonment. Possibly basedon this perception, the speculators attacked the exchange rate in 2000–2001. There-fore, the twin crises of 2000–2001 seem to be more related to the second-generationmodels.

Another variable that can be analyzed in conjunction with the second-generationmodels is inflation. In high inflation countries with a currency peg, inflation is a keydeterminant of competitiveness (Ozkan 2005). A loss of competitiveness might pre-vent policymakers from taking necessary measures to defend their currencies, suchas increasing interest rates. Economic agents being aware of this reluctance mightattack the domestic currency. If domestic inflation is above foreign inflation, com-petitiveness of the home country deteriorates because the exchange rate cannotadjust. This, in turn, damages the credibility of the peg. In the case of Turkey, in the

Fig. 13. Unemployment Rate

398



pre-1994 period, inflation was lower in 1993 than in 1992 (see Figure 14). However,high interest rates resulted in high and unstable nominal and real interest rates,which pushed public finances further along an unsustainable path. In the post-1994period, in contrast, inflation was consistently high and the current account wasmostly in deficit, including in 1999. Although the inflation level in 1999 was signif-icantly lower than it was in 1998, possibly reflecting the success of the stabilizationprogram, it was above the target rate that was set forth by the Central Bank.

Another important indicator of competitiveness is the real effective exchange rate.An overvaluation of the domestic currency leads to a loss of competitiveness. In thepost–capital account liberalization era, the Turkish economy experienced boom-bust cycles in international capital flows. The boom was associated with an appre-ciation of the currency and current account deficit. Given the exchange rate peg, realappreciations distort the relative prices in favor of imports versus exports, leading toa worsening of the current account balance (Figure 15). As is evident in Figure 16,between 1989 and 1993, there was a sustained tendency for the real exchange rateto depreciate. In contrast, before 2000–2001, there was an increase in this indicator,suggesting a loss of competitiveness, which is also evident from the trade balance.Especially in the post-1994 period, as the real exchange rate was appreciating, thegovernment was unwilling to abandon the peg and devalue as this would trigger asharp reversal of capital flows. However, this aggravated the currency misalignmentproblem. In this respect, the twin crises of 2000–2001, to some extent, carry the ele-ments of the second-generation models of currency crises.

Third-generation models focus on banking sector weaknesses. They argue that,due to the moral hazard problem, banks take unhedged foreign exchange positions

Fig. 14. Consumer Price Index (% Change)


399


in their loan portfolios. In the case of Turkey, there were severe weaknesses in thebanking sector before the twin crises of 2000–2001. For instance, the total bankingsector credit to GNP ratio and the real growth rate of loan portfolios were muchhigher in the post-1994 in comparison to the pre-1994 period. Credit expansion isevidence of banking sector weaknesses because the banks usually have imperfectinformation regarding the creditworthiness of borrowers. As can be seen in

Fig. 15. Current Account Balance (% of GNP)

Fig. 16. Real Exchange Rate

400



Figures 17 and 18, real credit growth was negative in 1998 and 1999. However, therewas a rapid increase in credits in 2000. In addition, total credits as a share of GNPwas much higher than in the pre-1994 period. This indicates an increase in the creditrisk in the banking system in the post-1994 period. In particular, the balance sheetstructures of the banks had weakened in 2000 compared to 1999 due to the rapidcredit growth in 2000.

Fig. 17. Total Credit / GNP

Fig. 18. Real Credit Growth


401


The increase in the volume of credits in the post-1994 period can be attributed tothe full deposit insurance system introduced by the government after the 1994 crisis.During this period, the sector was deregulated and granted deposit insurancewithout effective supervision (Akyüz and Boratav 2003). Indeed, as can be seen inFigure 19, this resulted in an increase in past-due loans in bank portfolios in 1998and 1999. Combined with the delays in banking reform, this led to a fragility in thebanking sector. Consequently, it can be concluded that the elements of third-generationmodels were present in the twin crises of 2000–2001. In the pre-1994 period,the evidence suggests the opposite. There was a fall in total loans to GNP ratio in1993. In addition, real credit growth was negative in 1993. This clearly indicates thatthere was no significant credit risk in the banking sector prior to the crisis of 1994.This reduces the possibility of a third-generation type crisis for the crisis of 1994.

Another indicator of banking sector fragility is the dominance of foreign currencyliabilities in the banks’ balance sheets. As can be seen in Figure 20, especially in thepost-1994 period, Turkish banks had explicit currency mismatches on their balancesheets as they borrowed in foreign currency and lent in local currency. In particular,during this period, banks replaced commercial lending with lending to the govern-ment. They borrowed from foreign markets and sold short-term funds to the Treasuryin return for government securities. Hence, the banking system was heavily depend-ent on the arbitrage margins offered by the high interest spreads between depositrates and T-bills caused by rapid inflation (Akyüz and Boratav 2003). The sector hadmostly unhedged its high exposure to exchange rate risk, which was also encouragedby the fixed devaluation rate built into the currency peg. This affected banks’ balancesheets by undermining the quality of their foreign currency loan portfolios.

Fig. 19. Past-Due Loans / Total Loans

402



As expected from the third-generation models,

2

just before the twin crises of2000–2001, investors sold domestic claims in anticipation of default possibly basedupon their realization that assets might no longer be sufficient to service liabilitiesin case of a shock. The ratio of liquid foreign exchange denominated liabilities tototal foreign exchange denominated assets was higher in both 1993 and 1999, indi-cating an increase in the foreign exchange risk. It is noticeable that the risk was morepronounced in the post-1994 period. Particularly in 1999, net foreign-exchangeopen positions of banks were an indication of rising exchange-rate risk in thesystem, which also rendered the system vulnerable to sudden reversals. Figure 21shows the banks’ liquid reserves to total assets ratio. As evident from the figure, theratio deteriorated in the post-1994 period. As can be seen, the liquidity position ofthe sector continuously deteriorated after 1994.

Given the weaknesses discussed above, the major problem in the banking systemcan be attributed to the inefficiency of the supervision of the banking sector. Indeed,there was a serious mistake made in the implementation of the supervisory andregulatory framework because the Treasury, which was involved in the regulationand supervision of the banking sector along with the Central Bank, had conflictinginterests. The treasury was in charge of inspecting the banks but was also obliged tofinance public sector deficits. Hence, it had less incentive to regulate the banks verystrictly as they held a sizeable amount of government securities (see Ozkan 2005).Overall, there is evidence regarding risk accumulation in the commercial bankbalance sheets before the twin crises of 2000–2001. A weak banking sector might

2

See, for instance, Aghion, Bacchetta, and Banerjee (2001) and Goldstein and Turner (2004).

Fig. 20. Foreign Liabilities / Foreign Assets


403


have set the stage for a self-fulfilling attack inducing speculators to anticipate thatthe government would not dare to increase interest rates to defend the currency.

The evidence presented in this section suggests that the twin crises of 2000–2001carried the elements of the third-generation models. The analysis has also revealedthat the Turkish economy was vulnerable to capital reversals due to the weaknessesin the banking sector in the post-1994 period. The analysis of the banking sector hashighlighted the risk accumulation in the banking system in the period preceding thecrisis, including the increase in currency mismatches and a rise in nonperformingloans. Hence, the banking system was highly vulnerable to exchange rate risk andcapital reversals. In this respect, the twin crises of 2000–2001 seem to carry theelements of the third-generation type of crises. The analysis has also shown that thecrisis of 1994 fits the first-generation models of speculative attacks, but it does notcarry the elements of second-generation and third-generation models of currency crises.

However, the twin crises of 2000–2001 do not quite fit the first-generation models,but carry the elements of both the second and third-generation models, albeit withmore elements of the third-generation models. Although the fiscal imbalances werenot completely resolved, the main vulnerability in the post-1994 period seems to bethe banking sector fragility stemming from risk accumulation in the balancesheets. In the absence of the elements of the first-generation crises, the presence ofa weak banking sector might have set the stage for a self-fulfilling attack, inducingspeculators to anticipate that the government would not dare to increase interestrates to defend the currency. In this respect, it is possible to argue that the twincrises of 2000–2001 had features relevant to the third-generation models of currencycrises.

Fig. 21. Banks’ Liquid Reserves / Total Assets

404



IV. DATA AND METHODOLOGY

A.

Data

A considerable number of variables can be used as indicators of vulnerability tocurrency crises. Essentially, the choice of which variables to select depends on theperceived causes of crises as well as on the variables suggested by the earlier studiesin the theoretical and empirical literature on currency crises. Accordingly, we selectedour variables so that they sufficiently represent the relevant theoretical modelsof currency crises. The data is monthly and spans the period between September1989 and April 2001. As Goldstein, Kaminsky, and Reinhart (2000) explain, monthlydata allows us to learn much more about the timing of the leading indicators. Thedata is obtained from the Central Bank of the Republic of Turkey’s Electronic DataDelivery System and the IMF’s International Financial Statistics Database.

A priori, in the published literature, there exists no clear-cut definition of whatshould constitute a currency crisis. As discussed earlier, under a peg or a managed-float, a central bank intervenes in the foreign exchange market to prevent exchangerate fluctuations. Hence, the exchange market pressure (EMP) index should ideallyincorporate, in addition to exchange rate changes, changes in reserves and interestrates. In the present analysis, an ad-hoc pressure index introduced by Eichengreen,Rose, and Wyplosz (1995), which consists of the weighted average of monthly ratesof changes of exchange rate, official reserves, and short-term interest rates are used.In the published literature, the EMP indices have included the exchange rate of thedomestic currency against either that of the US dollar or German mark (euro),depending on the features of the economy under study. The present study deviatesfrom the literature and uses an equally weighted basket of the nominal values of theDeutsche mark (euro) and the US dollar against the Turkish lira, which are the twoprevalent foreign currencies used in Turkey.

3

Following the mainstream literature,the United States is used as the country of reference. The index is calculated as follows:

(1)

where

∆

denotes monthly percent change,

e

t

denotes the equally weighted Turkishlira/Deutsche mark

4

–Turkish lira/US dollar nominal exchange rate basket,

i

denotesthe domestic short-term interest rate (three-month deposit rate),

5

corresponds to

3

Series are weighted by 0.5 each, following Kıpıcı and Kesriyeli (1997) who weighted these twocurrencies equally in an effort to calculate an index of real effective exchange rates for Turkey.

4

In January 1999, the euro was introduced and completely replaced Deutsche mark in December 2001.For the sake of consistency, we consider Deutsche mark for the whole period under study and use theofficial fixed parity (1 euro = 1.95583 Deutsche mark) to recalculate Turkish lira / Deutsche markexchange rate for the period after March 1999.

5

Three month deposit rate has been used as a proxy for short-term interest rates for Turkey as three-month T-bill rates are not available.

EMP e i i rt t t t t ( *) ,= + − −α β γ∆ ∆ ∆

it*


405


the same variable but for the United States (three-month US T-bill rate).

r

t

denotesthe ratio of international reserves (net of gold) to M1.

6

α

,

β

, and

γ

are the weightsof the components. An important issue regarding the index is the fact that each com-ponent has different volatilities, as shown in Table 1.

Reserve changes seem to be the most volatile component in all three periods andare likely to dominate the index. Therefore, the aggregation of the components hasto be conducted in such a way that prevents it from dominating the whole index.Eichengreen, Rose, and Wyplosz (1995) introduces precision weights to equalizethe weights of the three components to prevent the most volatile component domi-nating the index. More specifically, the three components are standardized by usingthe reciprocal of the standard deviation of the relative series, to equalize uncondi-tional volatilities. Therefore, the weights assigned to each component are estimatedas follows:

(2)

(3)

(4)

where,

σ

e

,

σ

i

, and

σ

r

denote the standard deviation of

e

t

, , and

r

t

, respectively.Hence, if a component has higher variance, a lower weight would be assigned to it.In the meantime, a component with a lower variance would gain a higher weight.

6

Eichengreen, Rose, and Wyplosz (1995) compare the domestic reserves with those of the UnitedStates. However, why reserve changes in the United States would be of interest in the case of Turkeyis questionable. Clearly, this appoach would render the EMP index subject to any idiosyncraticfluctuations in the US international reserves. Therefore US reserves are not included in the presentanalysis.

TABLE 1

Volatilities of the Components of the Exchange Market Pressure Index

Component

Standard Deviation

September 1989–May 1994

June 1994–March 2001

September 1989–April 2001

et 0.01 0.23 0.01 17.51 16.25 22.31

rt 1,141.45 5,508.42 12,798.96i it t *−

α σ ,=1

e

β σ ,=1

i

γ σ ,=1

r

( *)i it t−

406 the developing economies


A positive value of the index indicates an increased pressure in the exchangemarket that can stem from any combination of a devaluation, an expansion of theinterest rate spread, or a loss of international reserves. A currency crisis is consideredto occur when the EMP index exceeds a certain threshold value. We identify monthsin which the index of speculative pressure is at least 1.5 standard deviations abovethe sample mean as instances of speculative attacks, i.e., currency crises. The valueof 1.5 is used following Eichengreen, Rose, and Wyplosz (1996) and Herrera andGarcia (1999). Accordingly, a dummy variable is introduced to take the value of 1if a crisis occurs, and 0 otherwise. Figure 22 shows the graphical representation ofthe estimated EMP index and the threshold.

Turning to the explanatory variables, no single indicator captures all the informa-tion about the fiscal situation. In the published literature, the consolidated budgetbalance of the government, also referred to as the fiscal balance, is typically used asa share of GDP or GNP to proxy fiscal policy. However, the present study usesseveral novel indicators that proxy fiscal imbalances and the effectiveness of fiscalpolicy. Because the variables used in the analysis are monthly, they cannot beconsidered relative to GDP or GNP, which are available only in quarterly and annualfrequency. Instead, all fiscal variables are considered relative to the total revenues ofthe government. Instead of the budget balance, the budget expenditures to budgetrevenues ratio (EXR) is used. In addition, the noninterest budget expenditures tobudget revenues ratio (NIE) is used as an alternative indicator. This variable isinspired by the primary budget balance, which is derived by adding back net interestpayments to the overall budget balance. In analyzing the current fiscal performance,

Fig. 22. Exchange Market Pressure (EMP) Index

currency crises: the case of turkey 407


net interest payments should be excluded because net interest payments reflect thedebt built-up in the past, possibly by the previous governments. Hence, interestpayments on the stock of debt cannot be directly controlled by present fiscal policy.These payments are exogenously determined by interest rates and the stock of debtinherited. Therefore, NIE captures the current fiscal stance more accurately thanEXR, and is a key variable for the analysis of the dynamics of public debt and formedium-term fiscal sustainability. It is also useful for analyzing the effect of fiscalpolicy over the external or domestic macroeconomic balance because it preciselymeasures the effects of the current discretionary budget policy. To capture the impactof interest payments on existing debt, the interest payments to budget revenues ratio(IPR) is also used. In Turkey, the government relied heavily on borrowing in theperiod under study. To capture the impact of borrowing, total borrowing (TBR), con-sisting of domestic borrowing (DBR) and foreign borrowing (FBR), is consideredrelative to budget revenues. Also, net domestic borrowing as a share of total budgetrevenues is used to isolate the impact of domestic borrowing.

Following the existing empirical studies in the published literature (see e.g.,Kaminsky and Reinhart 1999), excess real Ml balances (ERM) is used to proxy thestance of monetary policy. Excess real M1 balances is a measure of excess moneycreation over the demand for money in the system. To calculate excess M1 balances,the following model is estimated:

(M1t/CPIt) = β0 + β1GDPt + β2INTt + β3TRENDt + εt, (5)

where the demand for real balances is determined by: real GDP, denoted by GDP(linearly interpolated from quarterly series); interest rates, denoted by INT (three-month bank deposit rates); and a linear time trend, denoted by TREND (used as aproxy for financial innovation and/or currency substitution; see Kaminsky andReinhart 1999). Excess real M1 balances is then estimated as the residuals of thisregression (see van Horen, Jager, and Klaassen 2006). Table 2 reports the resultsof this regression. As can be seen, the coefficient of GDP is positive, whereas theinterest rate coefficient is negative, as expected.

We also use domestic credit as a share of the monetary base of the central bank(DCB) to capture domestic credit expansion. In addition to these variables, four typesof risks are analyzed to assess balance sheet weaknesses: currency mismatches,capital structure, credit, and liquidity. Maturity mismatches are also important butdata is not available on the structure of the debt of the banking sector in Turkey.Therefore, this type of risk is not considered in the analysis. Banks in emergingmarket countries have explicit currency mismatches on their balance sheets as theyborrow in foreign currency and lend in local currency (Eichengreen and Hausmann1999). The domination of foreign currency denominated liabilities in the banks’balance sheets while assets are denominated in domestic currency is broadlyreferred to as liability dollarization. The presence of the pegged exchange rate



regime also contributes to liability dollarization as it implies that the exchange ratewould be stable over time and encourages banks to borrow in foreign currency.Liability dollarization might lead to severe losses when there is a sharp change inthe exchange rate.7 Hence, concerns about the banks’ ability to service their liabili-ties denominated in foreign currency quickly destabilizes confidence in the banksholding this debt and might lead to a bank run. Allen et al. (2002) explain that cur-rency mismatches can trigger shifts in capital flows that create pressure on reservesas net foreign currency debtors often seek to protect themselves against further realdepreciation by purchasing additional foreign currency assets. In this respect,currency mismatches are, theoretically, expected to be positively associated withexchange market pressure. Hence, the coefficient of the foreign assets to foreignliabilities ratio (FAL) is expected to have a negative sign. This ratio captures amismatch of the ratio of foreign currency–denominated liabilities to foreigncurrency–denominated liabilities on the balance sheets of commercial banks.

As loans are the riskiest assets, banks’ exposure to credit risk increases as theirvolume of credits increase. The ratio of total loans to total assets (TLA) is used tomeasure the exposure to credit risk. An increase in this ratio shows the increasedfragility in the banking sector and, therefore, is expected to increase the probabilityof exchange market pressure. In addition, the ratio of total loans to equity capital(LCA) is used. This ratio also shows the adequacy of the capital, and, therefore, isexpected to have a positive sign.

Capital structure mismatch risk results from relying excessively on debt financingrather than equity. The higher the size of capital, the larger is the buffer to absorb any

7 Currency mismatches generally are more pronounced in emerging economies because emergingmarkets agents, public and private, are often unable to borrow in local currency from nonresidents oreven, in many cases, from residents. As a result, obtaining capital for investment often requires takingon currency risk.

TABLE 2

Money Demand OLS Model Results

Variable Coefficient Standard Errors

GDP 0.05*** 0.03INT –5.10* 3.35TREND 2.49** 1.29Constant 3.34 4.19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

F-statistic 120.44* Sum of squared residuals 0.000R2 0.63 Log-likelihood –2,224.001

R2 0.63 Durbin-Watson statistic 2.284

*, **, and *** represent significance at the 1%, 5%, and 10% level, respectively.



financial distress such as unexpected losses due to customers’ defaults on loans.A heavy reliance on debt rather than equity financing leaves the banks less able towithstand revenue shocks. To proxy capital adequacy, the ratio of liabilities to capital(LIC) is used. This ratio is expected to be negatively related to exchange marketpressure. The ratio of deposits to capital (DCA) is used to measure solvency. Bothof these variables are expected to have positive signs.

Banks are exposed to liquidity shocks because their liabilities are usually short termand liquid, whereas their assets are longer term and are relatively less liquid. Liquidityrisk measures banks’ ability to meet unanticipated funds that are claimed by depositors.A high ratio of liquidity implies that the banks have a greater capacity to meet un-expected demand from creditors and, therefore, is negatively associated with thespeculative pressure on the exchange market. The ratio of liquid assets (cash andinvestment in marketable securities, such as treasury bills and bonds) to total assets(LTA) and the ratio of liquid assets to total deposits (LAD) are used to measure over-all liquidity. Both variables are expected to have a negative coefficient. However,LAD might also take positive values as they could be affected by decreases in bankdeposits, reflecting heavy deposit withdrawals.

As explained earlier, currency crises can be associated with capital reversals. Theportfolio investment to GDP ratio (PIG) is used to proxy short-term capital flows.GDP in the estimations is interpolated from quarterly data. PIG is expected to havea negative sign.

To capture the effect of global liquidity conditions, we use the three-month USreal treasury-bill rate (UST) as a measure of US monetary policy. Changes in USmonetary policy have been felt by developing countries through the effects on thecost and availability of funds (Arora and Cerisola 2001). Three-month US real treasury-bill rate is usually considered a key short-term risk-free rate that serves as a benchmarkfor pricing other high-yield assets in world capital markets. Hence, it is expected toreflect changes in global liquidity and economic conditions (Arora and Cerisola 2001).This variable is estimated as the difference between the three-month US real treasury-bill rate and the US consumer price index. An increase in US interest rates is expectedto lead to a capital outflow in Turkey and to increase devaluation expectations. Therefore,an increase in US interest rates is expected to have a positive impact on the likelihoodof the crises. As an alternative proxy to capture global liquidity conditions, the real interestrate differential (RID), defined as the difference between the three-month US deposit rateand the three-month Turkey deposit rate, is used. The higher the differential, the higherare the devaluation expectations. Hence, RID is also expected to have a positive sign.

However, international spot crude oil prices (OIL) is used as a variable to captureglobal economic conditions. In an oil-importing country such as Turkey, high oilprices could lead to domestic recessions and deteriorations in the current accountposition (Edison 2003). Therefore, this variable is expected to increase the likelihoodof a currency crises. Hence, it is expected to have a positive coefficient.



B. Methodology: Logit, Probit, and Linear Probability Models

Based on the identified crisis episodes, we will investigate the determinants of currencycrises in Turkey using logit, probit, and limited dependent regressions. Because thecurrency crisis index is constructed from ex post data on exchange rates, interest ratesand foreign reserves, rather than considering the explanatory variables as contem-poraneous with the crisis index, they are used after lagging one month to differentiategenuine leads from the effects of the crisis itself. This way, the currency crises are notexplained by the behavior of independent variables during a crisis. Also, assumingthat the market participants use most recent publicly available information to formtheir expectations and to decide on their course of action, using lagged values of theexplanatory variables can also address the possibility of nonsynchronous acquisitionor processing of the relevant information from market agents. Hence, variables repre-senting the global economic conditions are also used in their one-month lagged form.

The linear probability model (LPM) is simply an application of the multipleregression model (OLS) to a binary dependent variable, which takes on only twovalues: 0 and 1. The problem with a LPM is that the predicted values are not con-strained to be between 0 and 1. An alternative to estimating P(y = 1 | x) = β0 + xβis to model the probability as a function, G(β0 + xβ), where 0 < G(z) < 1. When G(z)is the standard normal cumulative distribution function, it is called a probit model.When G(z) is the logistic function, it is called a logit model. Both are similarfunctions increasing in z. Because probits and logits are estimated by maximumlikelihood, we cannot form an F-statistic to test exclusion restrictions. Instead, wecan use a likelihood ratio test. In the present analysis, five models are estimated withalternative variables with no multicollinearity:

Model 1:Ct = β1 PIGt−1 + β2 D(OIL)t−1 + β3 IPRt−1 + β4 D(DBR)t−1

+ β5 D(DCA)t−1 + β6 D(DCB)t−1 + β7 D(ERM)t−1 + β8 D(FAL)t−1

+ β9 D(LIC)t−1 + β10 D(NIE)t−1 + β11 D(TLA)t−1 + β12 D(NIE)t−1

+ β13 D(TLA)t−1 + β14 D(UST)t−1 + β15 RIDt−1 + µt; (6)

Model 2:Ct = β1 PIGt−1 + β2 D(OIL)t−1 + β3 IPRt−1 + β4 D(LAD)t−1 + β5 D(LCA)t−1

+ β6 D(DCB)t−1 + β7 D(ERM)t−1 + β8 D(FAL)t−1 + β9 D(TBR)t−1

+ β10 D(NIE)t−1 + β11 D(TLA)t−1 + β12 D(UST)t−1 + β13 RIDt−1 + µt; (7)

Model 3:Ct = β1 PIGt−1 + β2 D(OIL)t−1 + β3 IPRt−1 + β4 EXR t−1 + β5 D(DCA) t−1

+ β6 D(DCB) t−1 + β7D(ERM) t−1 + β8 D(FAL) t−1 + β9 D(LTA) t−1

+ β10 D(NIE) t−1 + β11 D(TLA)t−1+ β12 D(TBR)t−1 + β13 D(UST)t−1

+ β14 RIDt−1 + µt; (8)



Model 4:Ct = β1 PIGt−1 + β2 D(OIL)t−1 + β3 IPRt−1 + β4 EXRt−1 + β5 D(DBR)t−1

+ β6 D(DCB)t−1+ β7 D(ERM)t−1 + β8 D(FAL)t−1 + β9 D(LAD)t−1

+ β10 D(LCA)t−1+ β11 D(TLA)t−1 + β12 D(NIE)t−1 + β13 D(UST)t−1

+ β14 RIDt−1 + µt; (9)

Model 5:Ct = β1 PIGt−1 + β2 D(DBR)t−1 + β3 EXR t−1 + β4 D(DCB)t−1 + β5 D(ERM)t−1

+ β6 D(FAL)t−1 + Β7 D(LAD)t−1 + β8 D(LCA)t−1+ β9 D(NIE)t−1

+ β10 D(TLA)t−1+ β11 D(UST)t−1 + β12 RIDt−1 + µt. (10)

The models investigate the possibility of functional forms between the dichotomouscrisis index and the lagged values of the individual explanatory variables. Positivevalues of each coefficient imply that increasing the variable will increase the prob-ability of the crises, whereas negative values imply the opposite. The size of eachestimated coefficient reflects the relative effect of the variable on the predicted prob-ability of crises. Nonetheless, interpretation of the coefficient values is complicatedby the fact that estimated coefficients from a binary dependent model cannot beinterpreted as the marginal effect on the probability of crises. Hence, marginal effectsof the significant explanatory variables are estimated by taking the derivatives of theparameter estimates.

V. EMPIRICAL RESULTS

A. Unit Root Tests and Structural Breaks

Variables have to be stationary, especially in the case of limited dependentmodels. Otherwise, the econometric estimations might yield “spurious” results withno economic meaning. To test the stationarity properties of the variables, theAugmented Dickey-Fuller (ADF) and more powerful Phillips-Perron (PP) tests areused. The null hypothesis is that the series in levels are I(1) against the alternativehypothesis that they are I(0). Each series is also expressed in first differences so thatthe test for a unit root in the first difference of the series is equivalent to the null thatseries in levels are I(2) against the alternative hypothesis that they are I(2). Table 3reports the results of the tests. The results suggest that the variables are a mix of I(1)and I(0) series. A well-known weakness of the ADF and PP unit root test is theirpotential confusion of structural breaks in the series as evidence of nonstationarity.Perron (1989) argues that the power to reject the unit root decreases when the sta-tionary alternative is true and a structural break is ignored. To overcome this com-plication, Zivot and Andrews (1992) and Perron and Vogelsang (1992) use unit roottests that allow for one structural break, and Clemente, Montanes, and Reyes (1998)used unit root tests that allow two structural breaks in the mean of the series are

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TABLE 3

Augmented Dickey-Fuller and Philips-Perron Tests for Unit Root

Augmented Dickey-Fuller Test Phillips-Perron Test

DecisionLevels First Differences Levels First Differences

τT τµ τ τT τµ τ τi τµ τ τT τµ τ

FAL –2.35 –1.86 –1.48 –12.33* –12.37* –12.27* –2.37 –1.82 –1.47 –12.31* –12.32* –12.21* I(1)LCA 0.45 –0.73 0.56 –7.69* –7.39* –7.41* –4.20* –4.18* 0.23 –19.23* –17.23* –17.97* I(1)DCA –0.37 0.12 1.00 –5.32* –5.03* –4.97* –2.20 –1.65 0.72 –15.02* –14.23* –14.60* I(1)LTA –4.97* –1.17 –1.31 –17.52* –17.58* –17.54* –12.19* –4.18* –1.16 –98.16* –89.17* –43.06* I(1)PIG –1.87 –1.46 –1.67 –5.01* –5.02* –4.97* –10.58* –9.54* –9.18* –89.02* –89.0* –87.94* I(0)TLA –2.25 –1.21 –0.65 –14.34* –14.25* –14.26* –2.19 –1.08 –0.67 –14.24* –14.14* –14.11* I(1)LIC 0.74 –0.09 0.92 –6.11* –5.49* –5.45* –2.09 –2.06 0.67 –15.67* –15.00* –15.00* I(1)LAD –3.09 –0.99 –1.44 –17.33* –17.39* –17.33* –10.12* –2.96 –1.29 –39.90* –40.01* –34.14* I(1)DBR –4.42* –3.94* –2.02 –14.34* –14.38* –14.43* –4.30* –3.81* –1.60 –18.85* –18.64* –18.73* I(0)IPR –4.40* –2.74*** –0.93 –12.82* –12.85* –12.89* –4.48* –2.54 –0.54 –15.15* –15.15* –15.15* I(0)TBR –4.71* –4.15* –2.17** –14.15* –14.19* –14.24* –4.71* –4.03* –1.73*** –17.78* –17.75* –17.45* I(0)NIE –4.13* –3.29* –0.78 –15.04* –15.07* –15.11* –4.01** –3.00** –0.82 –16.35* –16.16* –16.35* I(0)EXR –4.08* –3.70* –0.59 –14.01* –14.04* –14.06* –3.95** –3.58* –0.45 –16.16* –16.89* –16.16* I(0)ERM –3.04 –3.22** –3.26* –5.59* –5.47* –5.48* –4.56* –4.04* –4.42* –16.08* –15.93* –15.97* I(0)DCB 0.27 –1.30 –1.24 –10.77* –10.62* –10.62* –0.03 –1.38 –1.25 –10.79* –10.69* –10.71* I(1)UST –2.02 –2.01 –1.26 –13.32* –15.37* –15.32* –5.58 –2.76 –1.43 –34.65* –34.51* –31.22* I(1)RID –2.13 –1.12 –0.23 –11.45* –12.32* –12.32* –1.11 –1.32 –0.45 –11.22* –11.32* –12.65* I(1)OIL –1.05 –0.21 –1.25 –13.21* –14.21* –13.11* –10.11* –2.32 –1.32 –30.40* –32.32* –25.21* I(1)

Notes: 1. τT represents the most general model with a drift and trend; τµ is the model with a drift and without trend; τ is the most restricted model without a drift and trend.

2. Numbers in brackets are the lag lengths and the bandwidths. In the tests, the lag length and the bandwidth are selected based on the Akaike Information Criterion (AIC) and the Newey-West Bartlett kernel.

*, **, and *** represent rejection of the null hypothesis at the 1%, 5%, and 10% level, respectively.



employed. This procedure tests the null hypothesis that there is a unit root with twostructural breaks against the alternative hypothesis that there is a stationary processwith two breaks. Shrestha and Chowdhury (2005) argue that the testing powerof the Perron-Vogelsang unit root test is superior to that of the Zivot-Andrews test.Therefore, the results of the Zivot-Andrews tests are presented only for comparisonin the present analysis. The Perron-Vogelsang and the Clemente-Montanes-Reyesunit root tests offer two models: (1) the additive outliers model, which captures asudden change in a series; and (2) the innovational outliers model, which allows fora gradual shift in the mean of the series.

According to Baum (2005), if the estimates of these tests provide evidence ofsignificant additive or innovational outliers in the time series, results derived fromADF and PP tests are doubtful, as this is evidence that the model excluding struc-tural change is clearly misspecified. Therefore, in applying unit root tests in timeseries that may exhibit structural breaks, one should consider the results from theClemente-Montanes-Reyes unit root tests rather than from the conventional unitroot tests (Baum 2005). Table 4 reports the results of the Zivot-Andrews, Perron-Vogelsang, and Clemente-Montanes-Reyes unit root tests. According to Baum (2005),if two-break tests’ estimates show that there is no evidence of a second break in theseries, Perron-Vogelsang techniques should be used to test for a unit root in thepresence of one structural break. As can be seen from the table, the different testsyielded different break dates in the series. However, most of the identified breakdates coincide with the currency crisis of 1994. Overall, the evidence suggests thatthe variables are a mix of I(0) and I(1) series. Although the presence of structuralbreaks introduces uncertainty as to the true order of integration of the individualseries, overall, the results suggest that PIG, IPR, EXR, and RID are stationary,whereas the rest of the series are I(1). Therefore, I(1) series are first-differenced andthe resulting (stationary) series are used in the estimations. First-differenced seriesare denoted with D such that D(ERM) denotes ERM in its first-differenced form.

B. Multicollinearity

Including highly correlated macro variables in a model could result in significantbias to the level of the parameters and might yield spurious results. If there is highmulticollinearity among two or more independent variables, the estimated co-efficients of these variables have high standard errors. This means that the expectedrelations among the dependent and independent variables cannot be estimated withhigh certainty. For this reason, to prevent the multicollinearity problem, the modelsformed in this research do not include correlated variables in the same model.Accordingly, in each model, the variable selection depends on the fact that there isno high-correlation variable in the models, which ensures the accuracy of the esti-mated parameters and the significance levels of each variable. The correlationmatrix in Table 5 reports the correlation coefficients among independent variables.

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TABLE 4

Unit Root Tests with Structural Breaks, September 1989–April 2001

VariableZivot-Andrews Test

Perron-Vogelsang Test Clemente-Montanes-Reyes Test

DecisionInnovative Outliers Additive Outliers Innovative Outliers Additive Outliers

t-statistic TB t-statistic TB t-statistic TB t-statistic TB1 TB2 t-statistic TB1 TB2

FAL –4.01 1994:04 –3.76 1996:07 –3.41 1997:01 –4.86 1994:02* 1996:07* –4.89 1994:10* 1997:01* I(1)LCA –1.54 1999:07 –4.45 1994:10 –1.69 2000:07 –0.53 1990:10 1994:10 0.14 1990:08* 1995:01* I(1)DCA –1.56 1999:07 –0.69 1999:10 –3.79 2000:06 –0.76 1994:07 1999:10* –2.36 1994:06 1999:09* I(1)LTA –5.37* 1992:08 –1.18 1995:01 –1.42 1994:12 –3.53 1994:05* 1997:08* –4.18 1994:04* 1997:11* I(1)PIG –13.11* 1992:01 –4.97* 1993:01 –5.56* 1994:01 –9.57* 1993:01 1994:02* –3.43 1992:12 1994:01* I(0)TLA –3.56 1998:09 –3.72 1998:07 –2.04 1998:10 –3.99 1990:11 1998:07* –2.64 1997:01* 1998:10* I(1)LIC –1.24 1999:07 –0.51 1999:10 –2.84 2000:07 –1.15 1994:10 1999:10* –2.75 1995:01* 1999:09* I(1)LAD –4.34 1994:04 –3.52 1994:02 –1.85 1994:09 –4.04 1994:05* 1997:08* –3.95 1994:07* 1998:11* I(1)DBR –5.56* 1995:01 –4.58* 1995:12 –3.06 1994:10 –4.67 1995:11* 1996:03* –2.61 1995:10* 1996:04* I(1)IPR –4.99 1998:01 –4.58* 1997:10 –3.53 1996:02 –5.58* 1994:12* 1997:10* –3.97 1996:02* 1998:10* I(0)TBR –5.03* 1996:01 –5.02* 1995:11 –3.93* 1995:10 –4.52 1995:11* 1996:03* –3.51 1995:11* 1998:10 I(1)NIE –3.74 1994:04 –3.04 1994:02 –1.80 1999:10 –4.02 1994:02* 1999:11* –1.05 1994:01* 1999:10* I(1)EXR –4.47 1996:05 –4.22 1998:11 –3.10 1996:02 –2.82 1996:03* 1998:11 –2.85* 1996:02* 1998:10* I(0)ERM –3.20 1995:02 –4.62* 1994:03 –1.12 2000:09 –3.91 1994:03* 1999:09 –3.76 1994:02* 1999:08 I(1)DCB –1.73 1994:04 –3.18 1993:12 –2.54 1994:10 –4.18 1991:06* 1994:04* –1.89 1991:05* 1994:09* I(1)UST –3.32 1993:01 –3.32 1993:01 –2.43 1993:01 –3.43 1993:04* 1997:06* –2.45 1993:04* 1997:06* I(1)RID –2.02* 2000:01 –2.65 2000:02 –2.45 2000:01 –2.01 1994:01 2000:02 –3.32* 1994:05 2000:02 I(0)OIL –0.87 1999:02 –1.21 1999:01 –1.32 1990:05 –0.23 1990:06 1999:02 –0.21 1990:05 1999:02 I(1)

Note: TB denotes structural breaks identified by the selected unit root tests.* represents rejection of the null hypothesis at the 5% level of significance; in the case of structural breaks, * represents significance at the 5% level ofsignificance.

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TABLE 5

Correlation Matrix

PIG IPR EXR D(DBR) D(DCA) D(DCB) D(ERM) D(FAL) D(LAD) D(LCA) D(LIC) D(LTA) D(NIE) D(TBR) D(TLA) D(UST) RID D(OIL)

PIG 1.0 –0.3 0.0 0.1 –0.1 0.1 0.0 0.0 0.2 0.0 0.1 0.1 0.0 0.1 0.1 –0.5 –0.1 –0.1IPR 1.0 0.8 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 –0.1 0.1 –0.1 0.5 0.0 0.2EXR 1.0 0.1 –0.1 0.0 0.1 0.0 0.1 –0.1 0.1 0.1 0.2 0.2 0.0 0.2 0.0 0.0D(DBR) 1.0 –0.1 0.0 –0.1 –0.1 0.1 –0.1 0.1 0.1 0.0 1.0 –0.1 0.0 0.1 –0.1D(DCA) 1.0 –0.2 –0.1 –0.1 –0.1 1.0 –0.1 –0.1 –0.1 –0.1 –0.2 0.0 0.0 0.1D(DCB) 1.0 0.2 –0.1 0.0 –0.1 0.0 0.0 –0.1 0.0 –0.1 0.0 0.0 0.1D(ERM) 1.0 0.1 0.0 –0.1 0.0 0.0 0.2 0.0 –0.1 0.1 0.2 0.0D(FAL) 1.0 0.0 –0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0D(LAD) 1.0 –0.1 1.0 1.0 0.1 0.1 0.3 0.0 –0.1 0.0D(LCA) 1.0 –0.1 –0.1 –0.2 –0.1 –0.1 0.0 0.0 0.1D(LIC) 1.0 1.0 0.2 0.1 0.3 0.0 0.0 0.0D(LTA) 1.0 0.2 0.1 0.3 0.0 0.0 0.0D(NIE) 1.0 0.0 0.2 0.0 0.0 –0.1D(TBR) 1.0 0.0 0.0 0.1 –0.1D(TLA) 1.0 0.0 –0.2 0.1D(UST) 1.0 0.1 –0.1RID 1.0 –0.4D(OIL) 1.0



C. Results of Logit, Probit, and Linear Probability Models

The estimates from the logit, probit, and limited dependent models generally tella consistent story. The signs of the coefficients are generally the same across themodels and the same variables are statistically significant in each model.

Table 6 reports the results of the logit models. The evidence suggests that D(DBR),D(ERM), D(FAL), D(LAD), D(LIC), D(LTA), and D(TBR) are significant withpositive coefficients, whereas PIG is significant with a negative sign, as expected.In probit models, as can be seen in Table 7, D(DBR), D(LAD), and D(TBR) aresignificant with positive coefficients. Also D(LTA) and PIG are significant with anegative sign, as expected. In linear probability models, as can be seen in Table 8,D(ERM), D(FAL), D(LAD), D(LIC), and D(TBR) are significant with positive signs.Additionally, PIG, D(TLA), and D(LTA) are significant with negative signs. as expected.

The findings also suggest that the budget expenditures to budget revenues ratio(EXR) and the noninterest budget expenditures to budget revenues ratio (NIE) arenot significant in any. Hence, it can be inferred that the occurrence of currency crisesin Turkey does not depend primarily on the ability of the authorities to maintain fiscalbalance. This is a finding that seems to contradict the first-generation models ofspeculative attacks. Also, the monetary expansion variable, DCB, is not significant.This can be explained by the fact that domestic credit was extended in Turkeyprimarily through cash advances from the Treasury until the end of 1994; hence,DCB might not accurately capture the impact of domestic credit expansion. Also, asexplained in Section III, domestic credit was minimized due to policy changes inmonetary policy in the aftermath of the currency crisis of 1994.

In linear probability models, the magnitudes of the coefficients are useful,whereas the coefficients of logit or probit models are not useful. Tables 6 and7 provide the factors for estimating the marginal effects of a unit change in the con-tinuous variables in the logit and probit coefficients. The factors have been estimatedfrom the partial derivatives of the coefficients. The magnitudes of the coefficients arenot directly comparable across the logit, probit, and linear probability models.Logit estimates can be divided by 4 and the probits estimates by 2.5 to make themcomparable to the linear probability estimates (Wooldridge 2003). AlthoughWooldridge (2003) argues that “the goodness of fit is less important than trying toobtain convincing estimates of the ceteris paribus effects of the explanatory varia-bles” (Wooldridge 2003, p. 560), the goodness-of-fit of the estimated LPM modelsbased on the R2 are satisfactory because the values are closer to 1 than 0. However,the quality of model specification in logit and probit specifications is assessed basedon the criteria of the maximized value of the log-likelihood function, pseudo-R2 andAkaike Information Criterion (AIC) criteria. The maximized value of the log-likelihood functions reveals that the models generally fit the data equally well. In themodels, the pseudo-R2 is the measure based on the log-likelihoods. The pseudo-R2



TABLE 6

Logit Maximum Likelihood Estimates

Model 1 Model 2 Model 3 Model 4 Model 5

PIGt-1 –3.36* –4.10* –3.23 –3.39 –3.47*(3.86) (4.11) (3.88) (3.92) (3.91)

IPRt–1 –2.06 –2.52 1.32 0.91(3.12) (3.11) (5.19) (5.31)

EXRt–1 –3.96 –3.34 –2.76(4.51) (4.40) (2.74)

D(DBR)t–1 6.67*** 7.99 7.90(3.84) (5.05) (4.95)

D(DCA)t–1 0.06 –0.00(0.19) (0.17)

D(DCB)t–1 0.01 0.00 0.01 0.01 0.01(0.04) (0.04) (0.04) (0.04) (0.04)

D(ERM)t–1 0.00*** 0.00*** 0.00 0.00*** 0.00***(0.00) (0.00) (0.00) (0.00) (0.00)

D(FAL)t–1 6.37 6.71*** 6.06 6.94*** 6.98***(3.90) (4.04) (6.06) (4.08) (4.06)

D(LAD)t–1 2.17*** 2.37*** 2.31***(1.12) (1.27) (1.17)

D(LCA)t–1 0.17 0.25 0.26(0.45) (0.44) (0.43)

D(LIC)t–1 3.46***(1.96)

D(LTA)t–1 –3.93**(2.17)

D(NIE)t–1 –3.42 –3.81 –2.63 –0.59 –0.84(5.60) (5.61) (6.09) (6.35) (6.19)

D(TBR)t–1 6.54*** 6.32***(3.45) (3.38)

D(TLA)t–1 –1.53 –2.04 –1.64 –1.67 –1.75(2.25) (2.21) (2.21) (2.22) (2.18)

Constant –2.54 –2.35 0.92 0.21 –0.09(1.66) (1.63) (4.01) (3.95) (3.52)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ME factor 0.06 0.06 0.06 0.06 0.06Pseudo R2 0.68 0.67 0.73 0.73 0.71AIC –30.43 –30.31 –28.30 –28.39 –28.13PT statistic 2.13** 2.12** 3.34* 3.42* 3.31*Log-likelihood –34.35 –34.34 –29.82 –29.97 –30.86

Note: ME, marginal effect, AIC, Akaike Information Criterion; PT, Pesaran-Timmermann.*, **, and *** represent statistical significance at the 1%, 5%, and 10% level, respectively.



TABLE 7

Probit Maximum Likelihood Estimates


PIGt-1 –2.21* –2.60* –1.97 –2.00 –2.11*(2.22) (2.36) (2.24) (0.37) (2.23)

IPRt–1 –1.38 –1.61 1.28 1.02(1.48) (1.46) (2.50) (2.56)

EXRt–1 –3.06 –2.71 –2.07(2.20) (2.15) (1.40)

D(DBR)t–1 3.51*** 4.13 4.07***(1.90) (2.56) (2.45)

D(DCA)t–1 0.05 0.00(0.11) (0.10)

D(DCB)t–1 0.01 0.01 0.01 0.01 0.01(0.02) (0.02) (0.02) (0.02) (0.02)

D(ERM)t–1 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00)

D(FAL)t–1 2.99 3.07 2.84 3.28 3.32(2.04) (2.09) (1.99) (2.12) (2.11)

D(LAD)t–1 9.76*** 1.11*** 1.06***(5.43) (0.62) (0.58)

D(LCA)t–1 0.14 0.14 0.16(0.23) (0.25) (0.24)

D(LIC)t–1 1.53(0.94)

D(LTA)t–1 –1.91***(1.05)

D(NIE)t–1 –1.05 –0.96 –0.23 0.49 0.36(2.79) (2.77) (3.13) (3.24) (3.23)

D(TBR)t–1 3.21** 3.36***(1.56) (1.72)

D(TLA)t–1 –9.65 –13.35 –9.16 –9.54 –10.57(12.54) (12.34) (12.38) (12.56) (0.39)

Constant –1.09 –0.97 1.51 1.14 0.83(0.76) (0.73) (1.94) (1.92) (1.74)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ME factor 0.09 0.09 0.09 0.09 0.09Pseudo R2 0.68 0.69 0.73 0.72 0.73AIC –30.48 –30.35 –28.59 –28.66 –28.22PT statistic 2.02** 2.05** 3.62* 3.64* 3.63*Log-likelihood –34.61 –34.41 –30.29 –30.40 –30.99

Note: ME, marginal effect; AIC, Akaike Information Criterion; PT, Pesaran-Timmermann.*, **, and *** represent statistical significance at the 1%, 5%, and 10% level, respectively.



reported in Tables 6 and 7 ranges from 0.67 to 0.73, implying that most of the vari-ation of the dependent variable is explained by the model explanatory variables. TheAIC compares the model with different degrees of freedom. A model with a lowervalue of AIC is judged to be preferable. Based on this criterion, Model 5 is the best

TABLE 8

Linear Probability OLS Estimates


PIGt–1 –3.01*** –3.17*** –2.26 –2.35 –2.68(1.65) (1.68) (1.75) (1.72) (1.63)

IPRt–1 –0.13 –0.16 0.18 0.15(0.14) (0.14) (0.26) (0.25)

EXRt–1 –0.38 –0.32 –0.21(0.23) (0.23) (0.12)

D(DBR)t–1 0.62* 0.64* 0.64*(0.16) (0.16) (0.16)

D(DCA)t–1 –0.00 –0.01(0.01) (0.01)

D(DCB)t–1 0.00 0.00 0.00 0.00 0.00(1.46) (0.00) (0.00) (0.00) (0.00)

D(ERM)t–1 0.00* 0.00** 0.00** 0.00** 0.00*(0.00) (0.00) (0.00) (0.00) (0.00)

D(FAL)t–1 0.43*** 0.41*** 0.39 0.43*** 0.44***(0.24) (0.24) (0.24) (0.24) (0.23)

D(LAD)t–1 14.37*** 13.52*** 13.78***(7.23) (7.08) (7.04)

D(LCA)t–1 0.00 0.00 0.00(0.04) (0.03) (0.03)

D(LIC)t–1 21.87***(12.05)

D(LTA)t–1 –22.69***(12.13)

D(NIE)t–1 –0.41 –0.38 –0.23 –0.24 –0.28(0.36) (0.37) (0.38) (0.37) (0.36)

D(TBR)t–1 0.54* 0.56*(0.16) (0.16)

D(TLA)t–1 –2.75 –3.04*** –3.31*** –3.09*** –3.03***(1.80) (1.79) (1.82) (1.77) (1.76)

Constant 0.14*** 0.16*** –0.49*** 0.43** 0.35**(0.08) (0.08) (0.21) (0.21) (0.17)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

R2 0.31 0.28 0.30 0.32 0.32R2 0.23 0.21 0.22 0.24 0.25AIC 0.17 0.23 0.20 0.16 0.15RSS 6.31 6.51 6.36 6.16 6.18

Note: AIC, Akaike Information Criterion; RSS, residual sum of squares.*, **, and *** represent statistical significance at the 1%, 5%, and 10% level, respectively.



among both the logit and probit specifications. The Pesaran-Timmermann test wasused to test for the predictive efficiency of the model. According to this test statistic,a significant association exists between the observed (actual crisis) and the model’sprediction of a currency crisis in all logit and probit models.

VI. INVESTIGATING THE IMPACT OF GLOBAL LIQUIDITY CONDITIONS

Because Turkey is a small economy, it is highly plausible that variables concerningglobal economic conditions have significant effects on the likelihood of currencycrises. Therefore, we consider some variables that have not been tested before in thecase of Turkey, such as indicators reflecting the changes in the US monetary policy,oil prices, and global liquidity conditions. Although these variables are expected tobe significant in an open economy like Turkey, conceptually, their impact on thegoodness of fit of the estimated models is also of interest.

Table 9 reports the results of the logit models. The evidence suggests thatD(DBR), D(FAL), D(LAD), D(LIC), D(TBR), D(UST), and RID are significant withpositive coefficients, which is in line with the theory. D(LTA) and PIG have negativecoefficients, as expected.

In probit models, as is evident in Table 10, exactly the same variables are signif-icant with the same coefficients. In linear probability models, as can be seen inTable 11, D(DBR), D(ERM), D(FAL), D(LAD), D(LIC), D(TBR), D(UST), and RIDare significant with positive signs. Also, D(LTA) and PIG have negative signs, asexpected. The results obtained from the three approaches are generally consistentand the coefficients obtained for the explanatory variables have the same sign.

The fact that global liquidity conditions, captured by D(UST), are significantverifies the belief that the changes in US monetary policy have an impact on theexchange market in emerging market economies. More specifically, an increase inthe US interest rates leads to an increase in devaluation expectations. Hence, itincreases the likelihood of a self-fulfilling speculative attack. Similarly, the fact thatRID is significant in all estimations with a positive sign suggests that global liquidityconditions captured by the real interest rate differential between the three-month USdeposit rate and the three-month Turkey deposit rate are indeed relevant in the caseof Turkish currency crises. The results reveal that increased devaluation expectationscaused by the higher interest rate differential do indeed increase the likelihood of acurrency crisis.

In addition, inclusion of these variables has improved the quality of model speci-fication in all specifications based on the criteria of R2, adjusted-R2, the maximizedvalue of the log-likelihood function, pseudo-R2, and AIC criteria. This serves asfurther evidence that global liquidity conditions have played a role in the currencycrises in Turkey in the sample period.



TABLE 9

Logit Maximum Likelihood Estimates


PIGt–1 –1.21* –1.44 –1.07* –1.12** –6.80*(2.81) (2.76) (2.98) (2.92) (2.58)

IPRt–1 –2.42 –2.59 –1.67 –1.81(3.17) (3.24) (4.85) (5.06)

EXRt–1 –1.13 –0.58 –1.69(4.06) (4.11) (2.74)

D(DBR)t–1 5.16* 5.56* 5.46*(2.86) (3.28) (3.24)

D(DCA)t–1 0.03 –0.01(0.18) (0.18)

D(DCB)t–1 0.01 0.01 0.01 0.00 0.00(0.04) (0.04) (0.04) (0.04) (0.04)

D(ERM)t–1 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00)

D(FAL)t–1 7.02* 7.29* 6.70* 7.43* 7.22*(3.98) (4.12) (3.97) (4.21) (4.14)

D(LAD)t–1 2.455** 2.44* 2.55*(1.191) (1.21) (1.22)

D(LCA)t–1 0.21 0.28 0.25(0.42) (0.44) (0.43)

D(LIC)t–1 3.671*(1.96)

D(LTA)t–1 –3.75*(1.98)

D(NIE)t–1 –5.67 –4.70 –5.29 –4.00 –3.62(7.21) (7.05) (7.64) (7.53) (7.49)

D(TBR)t–1 5.04* 4.77*(2.70) (2.45)

D(TLA)t–1 –12.21 –16.96 –13.43 –16.02 –14.51(24.34) (24.63) (24.01) (25.29) (24.86)

D(UST)t–1 6.06* 5.09* 5.37 4.37* 3.36*(9.78) (9.83) (10.34) (10.56) (10.16)

RIDt–1 0.03* 0.04* 0.03* 0.04* 0.04*(0.03) (0.03) (0.03) (0.03) (0.03)

D(OIL)t–1 –1.37 –2.14 –0.79 –2.32 –2.22(7.54) (7.68) (7.61) (7.95) (7.82)

Constant –4.05 –4.87 –3.08 –4.27 –3.77(2.99) (3.13) (4.70) (4.93) (4.71)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ME factor 0.07 0.07 0.07 0.06 0.07Pseudo R2 0.69 0.69 0.73 0.75 0.72AIC –45.08 –44.90 –41.92 –42.07 –41.68PT statistic 3.45* 2.34** 3.32* 3.33* 3.05*Log-likelihood –31.71 –31.70 –27.52 –27.67 –28.48

Note: ME, marginal effect; AIC, Akaike Information Criterion; PT, Pesaran-Timmermann.* and ** represent statistical significance at the 1% and 5% level, respectively.



TABLE 10

Probit Maximum Likelihood Estimates


PIGt–1 –8.40** –1.01 –5.94* –6.54 –5.18*(1.56) (1.58) (1.62) (1.64) (1.46)

IPRt–1 –1.38 –1.51 –0.43 –0.50(1.59) (1.62) (2.48) (2.61)

EXRt–1 –1.22 –0.97 –1.28(2.10) (2.17) (1.43)

D(DBR)t–1 2.72* 2.92* 2.91*(1.46) (1.58) (1.57)

D(DCA)t–1 0.03 0.00(0.11) (0.11)

D(DCB)t–1 0.01 0.01 0.01 0.01 0.00(0.02) (0.02) (0.02) (0.02) (0.02)

D(ERM)t–1 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00)

D(FAL)t–1 3.57* 3.80* 3.35 3.73* 3.68*(2.13) (2.22) (2.11) (2.22) (2.21)

D(LAD)t–1 12.46* 12.46** 12.72*(5.97) (6.17) (6.11)

D(LCA)t–1 0.15 0.17 0.16(0.24) (0.25) (0.25)

D(LIC)t–1 1.85*(0.98)

D(LTA)t–1 –1.94*(1.00)

D(NIE)t–1 –2.74 –2.30 –2.34 –1.58 –1.51(3.43) (3.41) (3.66) (3.72) (3.72)

D(TBR)t–1 2.61* 2.51*(1.36) (1.30)

D(TLA)t–1 –5.98 –8.94 –6.44 –8.37 –7.96(13.41) (13.72) (13.31) (13.99) (13.85)

D(UST)t–1 3.20* 2.77* 2.26 1.64* 1.31*(4.64) (4.65) (–2.26) (5.17) (4.89)

RIDt–1 0.02* 0.03* 0.02* 0.02* 0.02*(0.01) (0.01) (0.01) (0.01) (0.01)

D(OIL)t–1 1.29 1.74 0.66 1.61 1.53(3.78) (3.92) (3.93) (4.15) (4.11)

Constant –2.46* –2.85 –1.32 –1.85 –1.68(1.41) (–2.85) (2.45) (2.56) (2.41)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ME factor 0.08 0.08 0.08 0.08 0.08Pseudo R2 0.71 0.70 0.74 0.74 0.75AIC –49.59 –49.39 –46.12 –46.27 –45.85PT statistic 3.32* 2.24** 3.43* 3.42* 3.32*Log-likelihood –34.88 –34.87 –30.28 –30.43 –31.33

Note: ME, marginal effect; AIC, Akaike Information Criterion; PT, Pesaran-Timmermann.* and ** represent statistical significance at the 1% and 5% level, respectively.



TABLE 11

Linear Probability OLS Estimates


PIGt–1 –16.81* –18.06 –8.69 –10.14* –13.04*(16.01) (16.31) (17.41) (13.21) (14.84)

IPRt–1 –0.14 –0.15 0.12 0.08(0.15) (0.15) (0.24) (0.10)

EXRt–1 –0.32 –0.26 –0.20(0.23) (0.23) (0.14)

D(DBR)t–1 0.55*** 0.56* 0.56*(0.16) (0.23) (0.16)

D(DCA)t–1 0.00 –0.01(0.02) (0.01)

D(DCB)t–1 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00)

D(ERM)t–1 0.00** 0.00** 0.00* 0.00* 0.00*(0.00) (0.00) (0.00) (0.00) (0.00)

D(FAL)t–1 0.47* 0.46* 0.43* 0.48* 0.48*(0.24) (0.24) (0.24) (0.23) (0.23)

D(LAD)t–1 14.96** 14.27* 14.39*(7.29) (6.23) (7.14)

D(LCA)t–1 0.01 0.01 0.01(0.04) (0.03) (0.04)

D(LIC)t–1 22.65*(12.22)

D(LTA)t–1 –23.37*(12.32)

D(NIE)t–1 –0.41 –0.36 –0.23 –0.23 –0.27(0.37) (0.37) (0.39) (0.32) (0.37)

D(TBR)t–1 0.47* 0.48***(0.16) (0.16)

D(TLA)t–1 –2.49 –2.69 –2.95 –2.81 –2.77(1.88) (1.88) (1.91) (1.23) (1.85)

D(UST)t–1 0.14* 0.09* 0.05 0.04 0.05*(0.51) (0.52) (0.52) (0.52) (0.51)

RIDt–1 0.00* 0.00 0.00* 0.00 0.00*(0.00) (0.00) (0.00) (0.00) (0.00)

D(OIL)t–1 0.04 0.05 0.14 0.07 0.08(0.37) (0.38) (0.38) (0.38) (0.37)

Constant 0.00 –0.02 0.27 0.22 0.19(0.17) (0.17) (0.26) (0.21) (0.19)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

R2 0.26 0.26 0.28 0.28 0.26R2 0.15 0.15 0.18 0.16 0.16AIC –19.71 –19.83 –19.06 –19.46 –19.36RSS 8.51 8.53 8.29 8.32 8.47

Note: AIC, Akaike Information Criterion; RSS, residual sum of squares.*, **, and *** represent statistical significance at the 1%, 5%, and 10% level, respectively.



VII. CONCLUSIONS

This article has investigated the causes of currency crises in post–capital accountliberalization Turkey. The most striking finding of the present analysis is that thevariables concerning global liquidity conditions have significant effects on the like-lihood of currency crises. Strong evidence emerged that increases in US interestrates lead to an increase in the likelihood of a currency crisis in Turkey. Similarly,evidence suggests that an increase in the real interest rate differential between thethree-month US deposit rate and the three-month Turkey deposit rate increase thelikelihood of a currency crisis in Turkey. These findings reveal that increased devaluationexpectations caused by higher US interest rates lead to a self-fulfilling speculativeattack on the Turkish lira. This is also evident from the fact that the inclusion of thesevariables into the estimated models has improved the quality of the specificationsin all cases. This finding is not surprising as a large percentage of the capitalization ofthe Istanbul Stock Exchange is owned by foreign investors, who can withdrawtheir funds at any time if they that find investing elsewhere is more profitable.

Indeed, the results also suggest that the currency crises in Turkey are associatedwith capital reversals, or the “sudden stops” of these inflows, as suggested by Calvo(1998). This is also consistent with the results of several earlier studies on the causesof currency crises in Turkey by, inter alia, Akyüz and Boratav (2003), Kibritçio©lu,Köse, and U©ur (1998), Gazioglu (2003), Ghoshal (2006), and Cizre-Sakalliogluand Yeldan (2000), who suggest that capital movements led to the currency crises in1994 and 2000–2001. These findings lend support to Balkan and Yeldan (1998) andYentürk (1999), who argue that the Turkish financial system is under severe pressurefrom international speculators. In a similar vein, results are supportive of the analysisof the Turkish monetary policy by Balkan and Yeldan (2002), who explain that withthe opening up of the Turkish economy to the speculative foreign transactions, theCentral Bank is bound to take a passive role, and the domestic economy is trappedinto the vicious cycle of high real interest rates together with an overvalued domesticcurrency. The results also lend support to the views of Caprio and Summers (1993)and Stiglitz (1994), who argue that some degree of regulation is preferable topremature liberalization in developing countries.

Another finding of the present study is that both fiscal imbalances and bankingsector weaknesses are indeed among the determinants of currency crises in Turkey,with the correct signs in line with what the first-generation and the third-generationmodels of speculative attacks suggest. This is in line with the published literature onTurkish currency crises in which the fragility of the banking sector has frequentlybeen noted as one of the leading causes of currency crises in Turkey (see e.g.,Celasun 1998 and Özatay and Sak 2003).

This suggests that, sound fiscal management alone might not suffice to ensure themaintenance of the stability of the exchange rate and that the regulators should focus



equally on the promotion of balance sheet protection and impose tighter credit limitson foreign currency–denominated loans to limit exposures stemming from currencymismatches. Likewise, the regulators should limit the implicit promise of governmentbailouts of losses stemming from currency mismatches to discourage recourse toforeign currency–denominated loans. In addition, the Turkish Central Bank shouldregularly monitor currency mismatches to make informed decisions on time. Like-wise, because the results also indicate that capital outflows had an impact on thelikelihood of currency crises, practitioners and policymakers are advised to closelymonitor the capital movements for any sign of reversals, whereas governmentsshould not rely on hot money in their long-term plans, particularly for financingdeficits as any unanticipated reversal in capital flows would cause an increase inlikelihood of a currency crisis. In this respect, capital controls might be imposed tolimit capital flow fluctuations and to achieve economic stability.

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