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Exchange-Rate Exposure and the Financial Sector 31breaks to estimate exposure levels for firms in the financial sector overdifferent time periods. Secondly, we analyze the role of firm-specific factorson exposure level. Given the widespread problem of statistical insignificancewith the exposure coefficient, we use logistic regression approach to identifyfactors that are relevant for the probability of observing a significantexposure to exchange-rate fluctuations for a period that we identify as stable.The remainder of the paper is organized as follows. In section II, wediscuss the data sources and report the estimation results of exchange-rateexposure models. Estimation results for the logistic regression are discussedin section 3. The last section presents our main conclusions and suggestionsfor further research.2. Exchange Rate ExposureThe analysis is performed using monthly data at the firm level betweenJanuary 1995 and September 2006. Firm level data and stock prices areobtained from Istanbul Stock Exchange, 3 while exchange-rate data isobtained from the statistical data web page of the Central Bank of Turkey. 4Considering the high correlation between monthly U.S. dollar (USD)and Euro returns, the analysis is conducted using USD only. 5 Moreover,given that more than 90% of international transactions in goods and servicesinvolve these two exchange rates, 6 the exclusion of other exchange ratesshould not be assumed to be overly restrictive.Following earlier studies, exchange-rate exposure is calculated as thesensitivity of firm returns to exchange-rate movements. Specifically, weestimate the following market model:R i,t = α + β m R m,t + β s R s,t + ε i,t , (1)3 www.ise.gov.tr4 Daily data on USD and Euro prices in terms of YTL are used to obtain monthlyaverage prices.5 Between January 1999 and September 2006, the correlation coefficient betweenUSD and Euro rate of return was 0.89.6 For 2006, about 49% and 44% of exports used Euro and USD, respectively. Forimports, 38% and 59% of import-contracts were underwritten with these twocurrencies (source: Turkish Statistical Institute).

32Mehmet Nihat Solakoglu & Nazmi Demirwhere R i,t is the return on firm stock, R m,t is the return on the marketportfolio, and R s,t is the return on a portfolio consisting of USD only. 7Results of exchange-rate exposure estimations for the full sample areprovided in table 1. It appears that average exchange-rate exposure for thefirms in the financial sector is negative, though not overly significant asevidenced by mostly insignificant coefficient estimates. Given that theexchange rate is defined as the YTL value of a US dollar, a negativecoefficient indicates a decline in firm value as a result of local currencydepreciation. Interestingly, the “banking and special finance corporations”sub-sector faces with positive exposure to exchange-rate movements,implying that their firm value increases with a depreciation of localcurrency. 8 In other words, it appears that foreign-currency-denominatedassets of these institutions outweigh foreign-currency-denominatedliabilities. For the other sub-sectors though, average exposure beta isnegative with most significant coefficients having the negative sign as well,i.e. a depreciating TL leads to a decline in firm value.Table 1: Exchange rate exposure for full sampleSector # of Firms # Significant Direction (a) Beta σ beta Min Beta Max BetaBanks and Special FinanceCorporations 12 1 (+) 0.066 0.254 -0.253 0.576Insurance Companies 6 0 na -0.105 0.272 -0.568 0.283Financial Leasing andFactoring Companies 9 2 (-) -0.139 0.391 -0.769 0.291Holding and InvestmentCompanies 18 2 (+), (-) -0.125 0.367 -1.155 0.440Real Estate InvestmentsTrusts 9 2 (-) -0.195 0.280 -0.587 0.204Investments Trusts 20 1 (-) -0.063 0.291 -0.871 0.434(a): A plus sign indicates that the significant beta estimates are positive, while a negative sign indicates negative coefficient estimate. Given USD price ofYTL is used in calculations, a positive sign indicates that firm value increases with YTL depreciation.7 Since Solakoglu (2005b) study does not find strong evidence for asymmetricexchange rate exposure, we do not apply asymmetry in the estimations.8 However, as we indicated earlier, this interpretation is based on the sign of theaverage exposure, and not on the significance. The number of statistically significantcoefficients is limited implying that either firms are successful in hedging that riskor they do not have significant exposure.

Exchange-Rate Exposure and the Financial Sector 33Given that importers’ and exporters’ preferences for USD and Eurodiffer, 9 the movements in the USD-Euro exchange rate may also increasefirms’ exposure to exchange-rate risk. For financial firms, this parity risk canbe important if foreign-currency-denominated assets and liabilities differ inthe currency denominations. The market model is adjusted to reflect thisadditional risk, as shown by equation 2.R i,t = α + β m R m,t + β s R s,t + β p R p,t + ε i,t (2)In equation 2, R p,t is the return on USD-Euro exchange rate, while othershave the same explanation as in equation 1. Table 2 presents results formodel 2 for the period between February 1999 and September 2006. Ourfindings for the sensitivity of firm value to USD fluctuations are notsignificantly different from the previous findings. Although the averagecoefficient for parity risk implies a decline in firm value for all sub-sectorswhen USD depreciates against the Euro, the first sub-sector significantcoefficient has the only positive sign. A positive coefficient, in that case,implies that USD liabilities should be higher than assets so that depreciationcan increase firm value.Table 2: Exchange rate exposure and Parity risk for full sampleSector # of Firms # Significant (a) Direction (b) Beta ParityBanks and Special FinanceCorporations 12 1,1 (+) 0.043 -0.082Insurance Companies 6 0,1 na -0.037 -0.365Financial Leasing andFactoring Companies 9 2,3 (-) -0.134 -1.044Holding and InvestmentCompanies 18 3,2 (-) -0.138 -0.427Real Estate InvestmentsTrusts 9 2,0 na -0.233 -0.315Investments Trusts 20 1,3 (-) -0.065 -0.495(a) The number before the comma shows the number of significant for exposure beta, while the number after the commashows the number of significant parity risk estimates.(b): A plus sign indicates that the significant parity risk estimates are positive, while a negative sign indicates negativecoefficient estimate. Since parity is denoted by the price of Euro in terms of USD, a positive sign indicates an increase infirm value as a result of USD depreciation.9 See footnote 6. In addition, a survey of the Turkish textile industry indicated thatfirms prefer USD for imports but Euro for Exports. Even a firm with both exportsand imports indicate this preference as shown in Solakoglu and Ersin (2007).

34Mehmet Nihat Solakoglu & Nazmi DemirHowever, as before, we observe a widespread finding of insignificantexchange-rate exposure and parity risk. Since we do not observe a perfectoverlap between parity risk and exposure beta in terms of significance at thefirm level, we use significance of either coefficients as an indication ofexposure to the movements in the foreign exchange market.In both tables 1 and 2, we utilize all available data points. However,world economies and the Turkish economy encountered several importantcrises and alterations during the considered timeframe, such as theintroduction of the Euro. To determine the breakpoints in our series, we usethe Iterated Cumulative Sum of Squares (ICSS) algorithm introduced byInclan and Tiao (1994). The ICSS algorithm can be used to detect multiplebreakpoints in a time series by testing for volatility shifts. Let’s assume ε t isthe series in question with zero mean and σ 2 t as the unconditional variance.Inclan and Tiao (1994) define the cumulative sum of squares between time 1and k as:k2Ck= ∑εt,t=1where t = 1,...,T and k = 1,..,TThe centered and normalized cumulative sum of squares until time k isrepresented by the D k statistics.DkC=CkT−k, with D = DTT0=0If there is no volatility shift in the series, the plot of D k against k willoscillate around zero. On the other hand, with a volatility shift, we willobserve D k statistics drifting away from zero. Based on Inclan and Tiao(1994) study, an asymptotic critical value of 1.358 can be used to createboundaries to identify the point in time with a volatility shift. 10We use two series to identify the sudden changes in volatility. Thetime-points of the shifts can be used as the break points in the analysis. The10 Critical values are calculated from the distribution of D k under the the nullhypothesis of homogeneous variance. One can use the critical values to obtain upperand lower boundaries to detect volatility shifts. For details on the ICSS algorithmand some uses, please see: Inclan and Tiao (1994), Ewing and Malik (2005),Marcelo et al (2006).

Exchange-Rate Exposure and the Financial Sector 35series we consider are the return on market portfolio, as represented by theISE100 index, and the return on US dollar. The breakpoints are presented intable 3.Table 3: Return, Beta estimates, and BreaksSeries# ofBreaksPeriodAverageReturnStandardErrorExposureBetaParityBeta#significantSig ExpBetaSig ParityBetaExplanationISE100 2 Jan 1995 - Oct 2001 6.61% 28.30% -0.341 na 8,na -1.399 na Turkish financial crisis in 2001Nov 2001 - Sep 2006 4.27% 20.26% 0.2214 -0.1519 8,4 1.027 -1.899 Post-crisis periodUSD 5 Jan 1995 - Nov 1997 8.62% 25.86% ‐0.257 na 2,na -2.138 na*: Included in the Dec 2000-Nov 2001 periodDec 1997 - Sep 2000 7.73% 29.37% -2.453 na 13,na -3.990 na Asian crisis period: Russian crisisTurkish crisis first revealed itself in NovOct 2000 - Nov 2000* -11.59% 28.46% na na na na na of 2000; Technology bubble burst in USACrisis period in Turkey; EuropeanDec 2000 - Nov 2001 4.64% 28.50% -0.350 -1.385 11,19 -0.769 -2.752 Monetary Union (Euro): September 11Dec 2001 - Sep 2006 3.95% 20.07% 0.239 -0.126 10,5 0.957 -1.268 Post-crisis periodThere are 2 breakpoints identified by the ICSS algorithm when themarket portfolio (ISE100) index is used. The first period, January 1995 toOctober 2001, corresponds to before the 2001 crisis in Turkey. Given therecent introduction of the Euro, and the stickiness of transactionsdenominated in US dollars in Turkey, we decided not to include a proxy forparity risk. A comparison of pre and post-crisis periods shows that only asmall number of firms in the financial sector have significant exposure toexchange-rate movements. 11 Interestingly, exposure beta is positive postcrisis,while it is negative for the pre-crisis period. In other words, firm valueis positively impacted by the depreciation of the local currency in the postcrisisperiod and is negatively impacted in the pre-crisis period. In addition,the depreciation of USD against Euro in the post-crisis period leads to adecline in firm value. This may imply that assets are mostly denominated inUSD, and liabilities are mostly denominated in Euro.11 The reported number of significant cases will be underestimating the true numberas our dataset includes firms that exist as of 2006. However, several companies inthe financial sector, specifically in banking, went bankrupt after the crisis.

Exchange-Rate Exposure and the Financial Sector 37of the future cash-flow expectations. The last variable, ROE, represents thecurrent accounting performance of the firm. Since our dependent variable isbinary, we use logistic distribution as our link function to define theprobability of detecting significant currency exposure. That is:whererepresents the predicted value from the regression, andexp denotes exponential function in this equation. Through this linkfunction, the probability of detecting significant exposure ranges between 0and 1, and other problems associated with the linear probability model areeliminated (see, Greene, chap. 21, 1993).We believe firm size and age are important determinants ofexchange-rate exposure. Under the globally competitive business conditions,in order to have sustainable growth a firm must search for opportunities notonly in the local market but also in foreign markets. Hence, it is natural forlarger firms to have international transactions and a higher likelihood ofexposing themselves to exchange-rate risk. Similarly, younger firms areexpected to be in their growth stage and utilize all opportunities to grow.Hence, we should expect a positive relationship between size and currencyexposure. On the other hand, larger and older firms are expected to have thenecessary resources to hedge exchange-rate risk through several strategies.Hence, larger and older firms may not be sensitive to fluctuations inexchange rates. Thus, it may not be clear how size and age variables canimpact firm-level exposure (Dominguez and Tesar, 2006). As discussed inthe literature, family ownership can influence firm performance positively(see, for example, Demsetz and Villalonga (2001), Maury and Pajuste(2005), Maury (2006)). Moreover, these firms can have a lower exposure toexchange-rate movements as they can have a higher degree of risk aversionand thus take lower risks in international transactions.Table 4 presents estimation results for the logistic regression.Results are presented for all firms, and for firms segmented into two groupsof sectors with the first group – sectors 1,2 – including firms in “Banks andSpecial Finance Corporations” and “Insurance Companies”, and the secondgroups including the remaining sub-sectors. For all firms, we find that firmage has a negative effect on the probability of having significant exposure toexchange-rate fluctuations. This should not be surprising as discussedearlier. On the other hand, family owned firms appear to have a higher

38Mehmet Nihat Solakoglu & Nazmi Demirprobability than others, indicating that these firms are either less concernedabout the risk or that they do not have the required resources. The size of thefirm and other variables do not show any significant impacts.Table 4: Estimation results of Logistic regressionVariableSectorsAllSample 1,2 3,4,5,6Age of the Firm -0.8963*** 1.9394* -0.8240**0.1932 1.0095 0.3280Family Ownership (Binary) 0.8793** 5.2948** 2.0373***0.3770 2.0705 0.6533Size of the Firm 0.1058 -2.1045*** -0.57270.0960 0.7849 0.2333Market Cap/Book Value 0.1040 0.9036 0.02380.0732 0.6284 0.2237ROE 0.3920 19.6270** 0.27470.6276 7.7456 0.8917Log L -94.63 -14.54 -51.55***, **, * represents statistical significance at 1%, 5% and 10%, respectively.Sectors 1 and 2 are "Banks and Special Finance Corporations" and "InsuranceCompanies".Firm size is measured by the number of employees.Both Age and Size variables are in natural logarithms.For sub-sectors 1 and 2, firm age, family control and ROE all showpositive and significant coefficient estimates. That is, the level of probabilityis positively associated with these variables. It is surprising that older firmsare more likely to take currency risk, but perhaps older firms are the oneswith a larger share of family ownership. Higher returns on equity, one canargue, can indicate higher risk-taking for higher profits. The size of the firm,on the other hand, leads to a decline in that probability. The second segmentof sectors – sectors 3 to 6 – shows that only firm age and family controlimpact probability. While the probability of exposing firm valuesignificantly to exchange-rate fluctuations declines with age, it increaseswith family control as in the first segment.

Exchange-Rate Exposure and the Financial Sector 394. Concluding RemarksWe investigate the sensitivity of firm value to fluctuations in exchange ratesusing a market model for Turkey’s financial sector. With the exception ofthe “Banking and Special Finance Corporations” sub-sector, we find that adepreciation of the local currency leads to a decline in firm value. Moreover,only a small number of firms show significant exposure to exchange-ratefluctuations indicating that most firms are successfully protecting themselvesfrom currency risk.By using the ICSS algorithm, we identify the breakpoints in oursample. A stable period is selected based on the breaks to analyze the role offirm-specific factors on significant exposure to exchange-rate movements.We use the logistic regression method and consider only significantexposures. Results indicate that larger firms are more likely to be impactedless by exchange-rate movements. Family ownership, on the other hand,causes an increase in the probability of observing significant exposure. Firmage seems to have different effects based on the sub-sectors, while ROE hasonly a positive effect for the banking and insurance sectors.

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