Üçüncü Sektör Sosyal Ekonomi Dergisi Third Sector Social Economic Review 54(2) 2019 811-827 doi: 10.15659/3.sektor-sosyal-ekonomi.19.05.1145 Research Article Assessing The Impact Of Risk Factors On ’s Stock Returns Using The Egarch-M Model Banka Risk Faktörlerinin Türk Bankalarının Hisse Senedi Getirileri Üzerine Etkilerinin Garch-M Modeli İle Değerlendirilmesi

İsmail Erkan ÇELİK Dr., Doğuş University, Faculty of Economics and Administrative Sciences, Department of Economics, Hasanpaşa Mah., Zeamet Sok. No:21, 34722 Acıbadem - Kadıköy/ [email protected] https://orcid.org/0000-0002-2274-0750

Makale Gönderme Tarihi Revizyon Tarihi Kabul Tarihi 14.05.2019 23.05.2019 29.05.2019

Öz Bu çalışma, 1 Ocak 2002 - 4 Nisan 2019 tarihleri arasında haftalık banka düzeyindeki verileri kullanarak faiz oranı, döviz kuru ve kredi risk faktörlerinin Türk bankalarının hisse senedi getirileri üzerindeki etkilerini incelemektedir. Üssel GARCH ortalama (EGARCH-M) modeli 10 Türk ticari bankası için tahmin edilmiştir. Sonuçlar şunu göstermektedir: (i) farklı bankalar farklı tür risklere yatkındır ve risk katsayılarının büyüklüğü farklı özelliklere sahip bankalar arasında farklılık göstermektedir; (ii) kredi riski, kur ve faiz oranı risk faktörleri, hisse senedi getirileri üzerinde negatif ve anlamlı bir etkiye sahiptir; (iii) 6 banka için, getirilerdeki artış risklerdeki artışa bağlı olarak artmamaktadır; (iv) cari koşullu varyans (oynaklık) geçmiş sürprizlerin ve geçmiş oynaklığın fonksiyonudur ve bütün bankalar için zamanla değişmektedir; (v) Cari oynaklık geçmişe ilişkin haberlere yakın geçmiş sürprizlerinden daha duyarlıdır; (vi) geçmiş yeniliklerin, örnekteki bankaların yarısı için mevcut oynaklık üzerinde önemli asimetrik ve kaldıraç etkisine sahiptir; (vii) Pozitif ve negatif sürprizler banka getirilerinin oynaklığı üzerinde simetrik etkiye sahiptir; (viii) Küresel mali kriz sonrası kriz öncesi döneme kıyasla banka getirilerinin oynaklığı azalmış görünmektedir. Anahtar Kelimeler: Döviz Kuru Riski, Faiz Oranı Riski, Kredi Riski, Banka Getiri Oranları, EGARCH-M Abstract This study examines the effects of interest rate, exchange rate and credit risk factors on Turkish ’ stock returns using weekly bank-level data from 1 January 2002 to 4 April 2019. The first order autoregressive exponential GARCH in-mean (EGARCH-M) model is estimated for 10 Turkish commercial banks. The results indicate that: (i) different banks are prone to different types of risk and the magnitude of risk exposure coefficients differ across banks with different characteristics; (ii) credit risk, exchange rate and interest rate risk factors exert a negative and significant impact on stock returns of about six Turkish banks and bank portfolio; (iii) for 6 banks, increases in risk will not necessarily lead to an increase in the returns; (iv) current conditional variance (volatility) is a function of past surprises and past volatility and is changing by time for Önerilen Atıf /Suggested Citation Çelik, İ. E. 2019 Assessing The Impact Of Bank Risk Factors On Turkish Bank’s Stock Returns Using The Egarch-M Model, Üçüncü Sektör Sosyal Ekonomi Dergisi, 54(2), 811-827

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all banks; (v) the current volatility is more sensitive to old news than it is to the news about recent surprises in the market; (vi) past innovations have significant asymmetric and leverage effect on current volatility for half of the banks in the sample; (vii) the positive and negative surprises have a symmetric effect on the volatility of bank returns; (viii) volatility of bank returns seems to have declined in post global financial crisis period compared to pre-crisis period. Key words: foreign exchange risk, interest rate risk, credit risk, bank returns, EGARCH-M 1. Introduction In recent years, rapid changes that global and domestic markets have undergone have increased uncertainty and sparked interest in investigating the sensitivity of bank stock returns to bank risk factors. The factors that contributed significantly to uncertainties in global markets include trade war started by the US, lingering recovery in the world economy following global financial crisis, declining financial capital flows to developing markets, and tightening monetary policy by developed countries in particular. Increasing inflation rates, uncertainties related to new governmental system, indebtedness of private firms, slowdown in the rate of growth of economy and sustainability of balance of payments deficits are the sources of current uncertainties in the Turkish economy. Briefly, the subject is important because it is suspected that an increase in the level of uncertainty in global and domestic economy may increase the riskiness of individual banks and banking sector, in turn, lead to bank failures and hence crisis in the economy. This study examines the sensitivity of bank returns to bank risk factors, namely interest rate, exchange rate and credit risk in . The subject matter is important for both individual banks and macroeconomic perspectives. A banking sector which is well protected against risk factors is crucial for the stability of the whole economy and it is vital to achieve sustainable growth in the economy. An analysis of risk-return relationship is also important for the performance of individual banks since the adverse interest rate, exchange rate and credit shocks may end up with bank failures. Following the 2001 financial crisis, the Turkish banking sector has undergone a radical changes in its structure beginning with the transfer of 10 banks to the savings deposit insurance fund (SDIF). The Banking Regulation and Supervision Agency (BRSA) began to operate in August 2000 as a single authority for regulation and supervision of banks. To this end, BRSA determined the minimum capital adequacy ratio as 12% in Turkey. Briefly, in light of the lessons learned from the 2001, Turkey have enacted new laws, introduced new regulations and hence achieved to establish one of the strongest banking sector in the World. It is evident that the negative effects of the 2008 global financial crisis, which was felt strongly in the developed countries of the world economy was remained limited in the Turkish economy. Measures aimed at strengthening the Turkish banking sector have led the Turkish banking sector to overcome the 2008 global financial crisis easily and contributed a stable growth of the sector between 2002 and 2017 (Arabacı, 2018). The sector grew moderately until 2017, with the stability achieved in inflation, interest rates and exchange rates. However, the increased external debt of the sector has made banks vulnerable to foreign exchange risk. Today, the Turkish banking sector is the second largest banking system in Emerging Europe after Russia (Ekinci, 2016). There are several theoretical channels through which bank risk factors affect bank performance or bank stock returns1. Changes in interest rate and exchange rate risks can affect the value of a bank since investors readjust their portfolios based on changes in risks, thereby bank returns change (Kasman et al. 2011; Olugbode et al. 2014). Unexpected changes in interest rate and exchange rate also affect bank stock returns through changing revenues, costs of finance and hence profitability of banks (Saunders and Yourougou, 1990; Hyde, 2007; Park and Choi, 2011). Credit risk for banks is closely related to bank profitability and economic growth. Higher credit

1 See Kasman et al., (2011) and Olugbode et al., (2014) for additional theoretical linkages among stock returns and risk. 812

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risk increases the probability that loans will not be returned and hence banks’ profit and equity declines (Ekinci, 2016). Empirical studies on the effects of risk factors on stock returns have, in general, employed ordinary least square (OLS) and generalized least square (GLS) which ignores the time-varying risk properties of the data (Kasman et al. 2011). However, the estimates obtained from OLS are biased and inconsistent since linear models are unable to capture volatility clustering, the leverage and ARCH effects in the data. Later studies have employed GARCH models to accommodate the time-varying nature of bank return and risk factors data. Although GARCH models are suitable in modelling time-varying dynamics of the data, the non-negativity constraint imposed on the GARCH model is found to be too restrictive making the model incapable of capturing any non- linearity in volatility (Enders, 2014). Taken together, this study uses AR(1)-GARCH-M model is the estimation of the impact of interest rate, exchange rate and credit risk on bank returns. The model will be estimated for both individual banks and bank portfolio considering possible heterogeneities among risk sensitivities of individual bank returns. Furthermore, the empirical model of this study is also extended with a crisis dummy to measure whether volatility of banks returns has changed after the 2008 global financial crisis in addition to recent global and domestic developments caused. To this end, this study is organised as follows. Section 2 reviews the relevant empirical literature on the relationship between bank risk factors and bank returns. Section 3 provides the details on the empirical model and introduces the data which is subject to empirical analysis. Section 4 reports the results obtained from estimating the empirical model. The results include the estimates on the relationship between bank returns and interest rate, exchange rate and credit risks, and on the components of volatility of bank returns. Section 5 concludes the study. 2. Literature Review There is a vast empirical literature on the responsiveness of bank returns to bank risk factors. However, it is difficult to compare the evidence provided by these studies since they involve a variety of variables, sectors, estimation methods and models2. The review of the literature presented in this section focused on the empirical studies which employed ARCH-type models and examined the volatility and return relationship in banking sector. Early studies which employed ARCH type model on the subject includes Engle et al., (1990), Mansur and Elyasiani (1995), Flannery et al., (1997) Elyasiani and Mansur (1998). These studies were mainly concentrated on the impact of interest rate risk and market risk on bank returns and found that these risk factors had significant effects on bank returns. However, these studies did not measure the effects of exchange rate risk and credit risks on bank returns. Hooy et al. (2004) examined the responsiveness of bank returns to interest rate and exchange rate using GARCH-M model. The estimation results indicated that while Malaysian bank returns were not sensitive to these risks prior to and during the crisis, their risk responsiveness had increased after the capital control policy. In a similar study, Ryan and Worthington (2004) estimated GARCH-M model with market risk, interest rate and exchange rate risk variables using Australian daily bank data. The results indicated that only market risk and interest rate risk have significant impact on Australian banks. Elyasiani and Mansur (2005) report similar results for 52 Japanese banks over the period of 1986-1996. A significant strand of this empirical literature investigates the role of financial globalization and financial crisis in the analysis of stock return-risk relationship. The results of the studies on the impact of financial globalization on stock market volatility provide mixed results: Financial globalization is a stabilizing force, allowing for more efficient risk sharing (Kose et al. 2009), and

2 See Kasman et al. (2011), Sehgal and Agrawal, (2017), Olugbode et al. (2014), Dwumfour and Addy, (2019) for a review of empirical literature on the relationship between stock return and risk factors. 813

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hence decreasing volatility (Umutlu et al., 2010); leading to more efficient stock markets, with higher returns but no increase in volatility (Han Kim and Singal, 2000); but financial globalization may be a destabilizing factor in the economy, it may increase volatility (Bae et al., 2004; Esqueda et al., 2012) and lead to economic instability (Stiglitz, 2004). Cordella and Ospina Rojas (2017) comprise these contradictory views by arguing that the effects of financial globalization on stock market volatility are different in periods of global financial stability and instability. That is, financial globalization reduces the volatility of stock returns in tranquil times and it amplifies the volatility in periods of high uncertainty. Cordella and Ospina Rojas (2017) also find that the estimated effects of financial globalization can differ across countries depending on the frequency and magnitude of domestic shocks relative to the external ones. Financial globalization contributes to stability through diversification opportunities in case of domestic shocks, and leads to instability when external shocks dominate. Sehgal and Agrawal (2017) measure the time-varying properties of risk exposures for the Indian banks using GARCH model with weekly data over the period from 2004 to 2014. They found out that the effects of credit, equity, interest rate and exchange rate risks on bank returns vary across time periods and across banks with different characteristics. The results also showed that while equity and credit risks increase in the post-global financial crisis period, interest rate and exchange rate risks have declined. There are also a significant number of empirical studies examining the responsiveness of stock returns to risk factors. In an early study, Özçiçek (1997) examines the relationship between exchange rate volatility and stock market index volatility over the period of 1994-1997. The findings showed the presence of two way and asymmetric causal relationship among exchange rates volatility and stock market volatility. This relationship only holds for the cases in which exchange rate is increasing and/or stock market prices are falling. Yamak et al., (2018) investigate the relationship between exchange rate volatility and stock market index volatility employing the Granger causality test with regression and VAR model. The results of the study indicate the presence of a one-way causal relationship running from positive exchange rate volatility to stock market index volatility3. Kahyaoğlu and Kahyaoğlu (2017) analysed the relationship between volatility of BIST100 illiquidity and exchange rate volatility using Moon and Yu approach for the daily data over the period 01.02.01.2003- 18.08.2015. They have found that while the volatility of stock market illiquidity had no effect on exchange rate volatility, the latter seems to have a significant impact on the volatility of stock market illiquidity implying that investors’ decisions in the stock market are fundamentally affected by the volatility of exchange rate market. The authors also argued that in the context of , once an exchange rate has increased, foreign investors reduce their trading volume especially on bank stocks. The banking sector is more sensitive to volatility of exchange rate in Turkey. Tokat (2013) investigates the volatility in gold, exchange rate and stock prices using multivariate GARCH model. The results indicated that the volatility of ISI 100 index is not affected by the volatility in exchange rate market. Demirhan and Atış (2013) estimated the extent of exchange rate exposure of textile and leather firms listed in Borsa Istanbul (BIST) using GARCH model over the period of 2005-2011. The results showed that while dollar exposure had no statistically significant effect on the share of foreign sales and foreign assets, currency position, and firm size, euro exposure led to an increase in foreign sales and higher foreign assets over foreign liabilities. Çiçek (2014) investigates volatility spillover effects among foreign exchange, stock markets and government debt securities using multivariate EGARCH model over the period of 02.01.2004- 30.04.2008. The results indicate no flow of price spillovers from foreign exchange market to stock market on the contrary to the findings of Erdem et al. (2005). The volatility spillover effect

3 This study provides a survey of the studies on the causality relationship between exchange rate and stock market prices in Turkey. 814

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between stock market and foreign exchange market is bidirectional. Leverage effects are highly significant for all markets. Volatility shocks are highly persistent in the stock and foreign exchange markets but there is no long-run relationship between these markets. Kasman et al. (2011) examine the effects of changes in foreign exchange rate and interest rate on Turkish banks’ (13 banks listed on ISE) returns estimating GARCH (1,1) model with daily data over the period of 1999-2009. The results showed that exchange rate and interest rate changes have a significant impact on the conditional bank stock return. Interest rate and exchange rate volatility are the main determinants of the conditional bank stock returns volatility. The impact of market return seemed to have a stronger effect on bank stock returns than interest rate and exchange rate changes. Ekinci (2016) investigates the effects of credit, interest rate and foreign exchange rate risk on the Turkish banking sector profitability employing the GARCH model for the 18.01.2002-30.10.2015 period by using weekly data. He estimated two different GARCH (1,1) model. In the first model, while the mean equation is defined as the function of the return of industrial stock as a proxy for credit risk, foreign exchange rate and three-month deposit interest rates, the conditional variance part has the constant, ARCH and GARCH parameters. The second model he estimated involves only a constant in the mean equation, and variance equation involves square of foreign exchange and interest rate risk variable and credit risk variable in addition to ARCH and GARCH parameters. The results showed that while the return of industrial index and increase in exchange rates have positive effects on banks’ return, change in interest rate has a negative effect on bank’s profitability in general. In addition, GARCH parameter is stronger than ARCH parameter implying that the volatility of each stock return is more sensitive to its own lagged values than the news from the previous period. According to the findings of the study, shocks seem to have a persistent effect on stock returns as well. Taken together, the review of the empirical literature reveals a number of important aspects of the existing empirical literature on the impact of risk factors on bank returns. First, the effects of bank risk factors on bank returns vary across banks with different characteristics implying the presence of important heterogeneities among banks. For this reason, bank (disaggregate) level data should be used in risk exposure studies since empirical analysis carried out at aggregate (sector) level disguises a lot of information. Second, the results of the studies in the empirical literature also indicate that the responsiveness and volatility of bank returns to bank risk factors might be changing across time periods. That is, the so-called relationship may be different for pre- and post-global financial crisis periods. Third, empirical models on this subject should be accommodating the time-varying nature of volatility in bank returns and risk factor variables. In this sense, the GARCH type models seem to be the suitable modelling framework in the analysis of risk-return relationship. 3. Methodology and Data The impact of exchange rate and interest rate exposures on banks’ stock return is evaluated employing the following AR(1)-EGARCH(1,1)-M model:

2 푏푟푖푡 = 휃푖 + 휃1푖𝑖푟푡 + 휃2푖푒푟푡 + 휃3푖푥𝑖푛푑푟푡 + 휃4푖푏푟푖푡−1 + 훿 log(ℎ푖푡) + 휀푖푡 (1)

2 휀푖푡−1 휀푖푡−1 2 푙표푔ℎ푖푡 = 훼0 + 훼1 (| |) + 훼2 + 훽푖푙표푔ℎ푖푡−1 + 휑푖퐶푟𝑖푠𝑖푠푑푢푚푚푦 ℎ푖푡−1 ℎ푖푡−1 (2) where 푏푟푖푡 is the return of bank 𝑖 at time 푡; 휃푖 is the intercept term for bank 𝑖; 𝑖푟푡 is the interest rate risk; 푒푟푡 is the exchange rate risk; 푏푟푖푡−1 is the lag-dependent variable which is the return for bank 𝑖 at time 푡 − 1; 푥𝑖푛푑푟푡 is the credit risk measured as the rate of return of the industrial stocks; 2 ℎ푖푡 is the log of conditional volatility which indicates the risk pattern over time; 퐶푟𝑖푠𝑖푠푑푢푚푚푦 is a dummy variable which represents the 2008 global financial crisis dummy variable which

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takes zero for pre-crisis period (before October 2008) and one for afterwards, and 휀푖푡 is the error term. Equation (1) is the mean equation which models the banks’ return as a function of credit risk, exchange rate risk, interest rate risk, conditional variance and the lag-dependent variable. 2 Equation (2) is the variance equation in where 푙표푔ℎ푖푡 is the log of the conditional variance (current volatility) and it is an asymmetric function of constant term, absolute value of ARCH term (past innovations, past shocks), ARCH term, GARCH term (past volatility) and crisis dummy. The coefficients in Equation (2) can be interpreted as follows: 훼0 is the constant component of volatility. 훼1 measures the asymmetric effects of past shocks on current volatility. 훼1 is called the ARCH effect and it indicates the tendency of shocks to persist. If 훼2 < 0, it is called leverage effect implying that negative and positive news have different impacts on volatility. 훽 is called the GARCH term. It is the persistence parameter which measures the impact of past volatility (old news) on current volatility. The data employed in this study is obtained from the Finnet Data Delivery System for a sample of ten Turkish stocks listed on the Borsa Istanbul (BIST). It is weekly data for the period from 01.01.2002 to 04.04.2019 with 891 observations, except for daily closing prices of Halkbank, Vakıfbank and due to data availability4. The empirical counterparts of the variables given in Equation (1) are defined as follows. The return of bank 𝑖 at time 푡, 푏푟푖푡 = ln (푏푝푖푡/푏푝푖푡−1) where 푏푝푖푡 is the closing price of bank 𝑖’s index at time 푡. Considering the fact that the higher the profit for industrial companies, the higher the stock return in industrial sector and hence the lower the credit risk exposure of banks, the credit risk variable, 푥𝑖푛푑푟푡, is calculated as the rate of return of industrial index (푥𝑖푛푑푟푡 = ln (푥𝑖푛푑푝푡/푥𝑖푛푑푝푡−1) where 푥𝑖푛푑푝푡 is the industrial index at time 푡 (Ekinci, 2016). To construct the interest rate risk variable, the data on 3-month deposit interest rate is used since the deposits are the main financing sources for Turkish banks and the average duration of deposits is around 3 months (Ekinci, 2016). The foreign exchange rate (푒푟푡) data is also obtained from the Finnet and it is defined as equally weighted average of the US dollar and the Euro. As Choi et al. (1992) and Olugbode and Pointon (2014) argued, expected changes would have no effect on asset prices in efficient markets and only the unexpected changes in interest rate and exchange rate affect stock returns. The interest rate risk (𝑖푟푡) and the exchange rate risk (푒푟푡) variables are calculated as unexpected changes in interest rate and exchange rate using the autoregressive integrate moving average (ARIMA) model. Based on the Akaike information criteria, ARIMA(4,1,4) is chosen for the exchange rate and ARIMA(9,1,4) for the interest rate. The residuals obtained from the estimation of the chosen ARIMA models are used as a proxy for the unexpected changes in interest rates and exchange rates. Table 1. Descriptive Statistics Jarque- Mean Max Min SD Skewness Kurtosis Bera ADF - Yapı Kredi 0.0012 0.5494 -0.4418 0.0636 0.0830 13.631 4196.74* 10.814* - Vakıfbank 0.0007 0.2933 -0.2386 0.0579 0.0964 5.783 226.96* 17.533* - Seker bank 0.0019 0.6740 -0.2936 0.0670 1.1403 16.313 6772.64* 27.836* - Qnbf bank 0.0051 0.4902 -0.1974 0.0630 1.8005 13.195 4340.03* 19.835* - İs Bank 0.0021 0.4285 -0.2576 0.0572 0.3756 7.988 944.61* 19.845* - Icbc bank 0.0032 0.7991 -0.8134 0.0847 -1.0321 29.999 27221.11* 10.669*

4 The data periods are 24.04.2017-04.04.2019 for Halkbank, 23.09.2004-04.04.2019 for Denizbank, 10.11.2005-04.04.2019 for Vakıfbank. 816

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- Halkbank -0.0002 0.2167 -0.2324 0.0584 -0.0261 4.105 31.82* 26.568* - Garanti 0.0033 0.3137 -0.2150 0.0585 0.1366 5.121 169.83* 20.796* - Denizbank 0.0049 0.8895 -0.4325 0.0815 3.2847 32.402 28703.05* 27.000* - 0.0029 0.2321 -0.2171 0.0557 -0.0434 4.837 125.54* 32.517* - Bankindex 0.0022 0.3656 -0.2059 0.0508 0.2402 6.991 599.77* 20.207* - IR -0.0000 0.1069 -0.1204 0.0221 -0.0297 7.611 787.71* 29.615* - ER -0.0000 0.1059 -0.1240 0.0176 0.4422 9.075 1395.90* 30.048* - XINDR 0.0028 0.2118 -0.2012 0.0335 -0.5683 7.366 753.80* 19.455* Note: Max, Min, SD, IR, ER, XINDR, stand for maximum, minimum, standard deviation, unexpected changes in interest rates, unexpected changes in exchange rate, industrial index return, respectively. * indicates the significance level at 1%.

Table 1 presents the descriptive statistics for the returns of the individual banks, Bank Index, industrial index and unexpected changes in interest rate and exchange rate variables. The skewness measures are positive for all bank returns except for Icbc bank, Halkbank, Akbank indicating non-symmetric distribution. The distribution of IR and XINDR variables are also negatively skewed. The kurtosis measures have relatively large values exceeding the normal value of three. This indicates that the underlying data are highly leptokurtic, or more peaked around the mean with fat-tails compared to the Gaussian distribution. Augmented Dickey-Fuller (ADF) statistics given in the last column indicates that the null hypothesis of unit root has been rejected at one percent level and hence all variables in dataset are stationary. The Jarque-Bera statistic shows that normality null hypothesis has been rejected for all variables at one percent level of significance implying that all series are likely to be volatile and change rapidly in an unpredictable way (Olugbode et. al., 2014). 4. Empirical Findings In this study, the impact of interest rate, exchange rate and credit risk on Turkish banks’ returns has been determined using the AR(1)-EGARCH-M model given in Equations (1) and (2). As mentioned above, the GARCH-type methods of estimation is very useful to capture the time- varying properties of the series in the presence of clustering of observations, and to model volatility in the series. To determine the suitability of GARCH type of a model in an empirical analysis of bank returns in Turkey, the ordinary least square (OLS) regression given in equation (3) was estimated for individual banks and bank portfolio index, and the residuals of the equation were then tested for the presence of autocorrelation, heteroscedasticity and normality.

푏푟푖푡 = 휃푖 + 휃1푖𝑖푟푡 + 휃2푖푒푟푡 + 휃3푖푥𝑖푛푑푟푡 + 휀푖푡 (3) OLS regression results has been provided in Table 2. The results indicate that the credit risk and exchange rate risk explain an important part of the change in individual bank returns and bank portfolio index. While exchange rate risk has a negative and significant effect on bank returns in seven out of eleven cases except for Denizbank, credit risk affects returns in nine out of eleven cases. On the other hand, although interest rate risk has a negative effect on return for all banks except for Akbank, it is only significant for bank portfolio index. The diagnostic results provided in the last two columns of Table 2 show that equation (3) suffers from serious autocorrelation and heteroscedasticity problems in error terms. The Breusch-Godfrey Lagrange Multiplier (LM) test results (×2(1)) reject the null of no serial correlation in six out of eleven cases. The results of the ARCH test confirm the presence of serious heteroscedasticity in ten out of eleven cases. Although

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it has not been reported in Table 2, the residual normality is rejected by Jarque-Berra statistics for all equations.

Table 2. OLS regression results on the determinants of bank return

2 2 푏푟푖푡 𝑖푟푡 푒푟푡 푥𝑖푛푑푟푡 Constant 푅̅ × (1) ARCH(1) Yapı Kredi -0.0451 -0.5743* 1.0754* -0.0018 0.4140 1.4752 8.5906* (0.0740) (0.1005) (0.0526) (0.0016) Vakıfbank -0.1583 -0.0862 0.1694*** 0.0005 0.0109 0.0195 16.1660* (0.1005) (0.1335) (0.0761) (0.0022) Seker bank -0.0753 -0.4477* 0.9986* -0.0009 0.3075 0.4605 83.7040* (0.0847) (0.1150) (0.0602) (0.0019) Qnbf bank -0.0596 0.0414 0.7530* 0.0030 0.1554 3.2264*** 10.6476* (0.0880) (0.1194) (0.0626) (0.0019) Is Bank -0.0875 -0.4401* 1.1516* -0.0011 0.5453 4.2143** 36.5763* (0.0586) (0.0795) (0.0417) (0.0013) Icbc bank -0.1551 -0.3008 1.1441 0.0001 0.2308 4.6170** 5.6165** (0.1129) (0.1533) (0.0803) (0.0025) Halkbank -0.1680 0.0694 0.1518*** -0.0004 0.0049 1.6950 3.2848*** (0.1081) (0.1418) (0.0813) (0.0023) Garanti -0.0615 -0.4056* 1.1554* 0.0001 0.5159 10.3901* 57.0271* (0.0618) (0.0839) (0.0439) (0.0014) Denizbank -0.1337 0.3585*** 0.0424 0.0049*** 0.0022 0.2603 2.3540 (0.1388) (0.1851) (0.1038) (0.0030) Akbank 0.0189 -0.3624* 1.0330* 0.0001 0.4526 11.0189* 25.0056* (0.0626) (0.0850) (0.0445) (0.0014) Bankindex -0.0819*** -0.4097* 1.1067* -0.0009 0.6350 5.9649** 45.6004* (0.0466) (0.0633) (0.0332) (0.0010) N. of significant cases 1/11 7/11 9/11 1/11 6/11 10/11 Note: 푅̅2, AR(1), ARCH(1) stand for adjusted R-square, auto-correlation test and ARCH test, respectively. Numbers in parentheses indicate standard errors *, **,*** indicate the significance level at 1%, 5% and 10% respectively.

Taken together, the presence of residual heteroscedasticity and autocorrelation violates the OLS classical assumptions producing inefficient estimates and thereby making statistical inferences based on t-test and F-tests unreliable. To overcome the deficiencies of the regression model, this study employed EGARCH-M model to estimate the impact of interest rate, exchange rate and credit risk exposures on banks’ return. The AR(1), the lag-dependent variable, is included in the mean equation of the EGARCH-M model to remove serial correlation in error terms. Table 3 reports the findings related to the mean equation (Eq. (1)) obtained from the estimation of AR(1)- EGARCH-M model given in equations (1) and (2).

Table 3. Exposure of Turkish banks to unexpected changes in exchange rate and interest rate, and to credit risk

2 푏푟푖푡 log(ℎ푖푡) 푒푟푡 𝑖푟푡 푥𝑖푛푑푟푡 Constant 푏푟푖푡−1 Yapı Kredi -0.0010 -0.1301** -0.5166* 1.0755* -0.0085 -0.0518** (0.0021) (0.0609) (0.0675) (0.0383) (0.0142) (0.0221) Vakıfbank 0.0037* -0.1513** -0.1699 0.0968 0.0198* - (0.0001) (0.0731) (0.1177) (0.0668) (0.0001) Seker bank -0.0023 -0.0560 -0.3938* 0.9576* -0.0155 0.0619** (0.0032) (0.0701) (0.0761) (0.0474) (0.0207) (0.0250) Qnbf bank -0.0141*** -0.0197 0.1690*** 0.7854* -0.0801*** - (0.0082) (0.1017) (0.0955) (0.0494) (0.0482) Is Bank -0.0037*** -0.1177** -0.3958* 1.1093* -0.0267*** - 818

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(0.0022) (0.0539) (0.0625) (0.0312) (0.0154) Icbc bank -0.0037 -0.1795*** 0.1430 0.9557* -0.0211 - (0.0032) (0.1044) (0.1054) (0.0501) (0.0191) Halkbank -0.0081 -0.1751*** 0.0159 0.0742 -0.0463 - (0.0104) (0.1019) (0.1365) (0.0864) (0.0602) Garanti 0.0035 -0.1699* -0.4474* 1.1381* 0.0236 -0.1331* (0.0041) (0.0449) (0.0667) (0.0378) (0.0271) (0.0228) Denizbank 0.0173** 0.0317 0.2670 -0.0344 0.0975* - (0.0069) (0.1019) (0.2036) (0.0826) (0.0363) Akbank 0.0032 -0.0239 -0.4820* 1.0188* 0.0205 -0.1119* (0.0029) (0.0551) (0.0621) (0.0389) (0.0194) (0.0230) Bankindex -0.0022 -0.1043* -0.4209* 1.1216* -0.0164 -0.0843* (0.0045) (0.0310) (0.0508) (0.0246) (0.0319) (0.0200) N. of significant cases 4/11 7/11 7/11 8/11 4/11 5/11 Note: Numbers in parentheses indicate standard errors *, **,*** indicate the significance level at 1%, 5% and 10% respectively.

The examination of Table 3 indicates that more than half of the banks and bankindex have significant exposure coefficients. The unexpected change in exchange rates have negative and significant effect on bank returns in 7 out of 11 (7/11) cases indicating that most banks suffer significant losses from unexpected depreciation of Turkish lira. Similarly, unexpected changes in interest rates have also negative and significant effect on bank returns except for Qnbf bank, implying that bank returns decline when unexpected changes in interest rates increase. The significant and positive coefficient for Qnbf bank indicates that the bank benefits from an increase in unexpected change in interest rates. The impact of credit risk on bank returns is negative and statistically significant in 8/11 cases, indicating that a rise in credit risk leads to a fall in average bank returns. As explained above, the percentage change in industrial index is used as a proxy for credit risk. In this sense, an increase in industrial index return indicates a decline in credit risk. Overall, the results obtained for the relationship between risk exposures and bank returns are in line with the literature (Molyneux and Thornton, 1992; Kasman et al. 2011; Mileris, 2012; Ekinci, 2016).

2 The coefficient of log(ℎ푖푡) is called the risk-return trade-off parameter, and measures the relationship between bank returns and volatility. Examination of Table 3 reveals mixed results for the relationship between volatility and bank returns. In 7/11 cases, the risk-return parameters are statistically insignificant for most banks indicating that increases in risk will not necessarily lead to an increase in the returns. However, the risk-return parameter is significant in 4/11 cases and its sign is negative for two banks while it is positive for two banks. The positive risk-return parameter means that an increase in the conditional variance (i.e. changes in the volatility of returns) due to an increased risk will lead to a rise or fluctuations in bank returns. The negative risk-return parameters imply the presence of an inverse relationship between an increase in volatility and an average bank returns in contrast to the prediction of classical finance theory. However, there is an amplitude of studies that report the presence of a negative relationship between risk and return5. Taing and Worthington (2005) argue that the sign of trade-off parameter is closely related to the relative importance of unsystematic risk in total risk since the trade-off parameter is a measure of total risk (systematic and unsystematic risk). In this sense, if changes in volatility stem from negative shocks to the unsystematic risk, then the sign of trade-off parameter will be negative. Elyasiani and Mansur (1998) and Ryan and Worthington (2004) also provide an evidence that there is a negative relationship between risk and return for banks. Furthermore, while the constant term is only singificant four out of eleven cases, the coefficient

5 Olugbode et al., (2014) provide a review of the literature on the negative relationship between risk and return. 819

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of the lag-dependent variable is significant in five out of elevan cases. The latter implies the presence of important dynamics in determination of bank returns. Table 4. Volatility estimates

훼0 훼1 훼2 훽 휑 Yapı Kredi -0.5387* 0.2370* -0.0282 0.9348* -0.0882* (0.1015) (0.0268) (0.0211) (0.0161) (0.0221) Vakıfbank -0.0370* -0.0053 -0.0685* 0.9927* - (0.0028) (0.0027) (0.0081) (0.0002) Seker bank -1.1031* 0.0684** 0.1534* 0.7973* -0.2813* (0.2232) (0.0300) (0.0249) (0.0402) (0.0519) Qnbf bank -2.3617* 0.2162* 0.1484* 0.6234* 0.0612** (0.4951) (0.0433) (0.0269) (0.0799) (0.0309) Is Bank -0.4480* 0.1888* -0.0122 0.9495* -0.0536* (0.1160) (0.0321) (0.0180) (0.0175) (0.0200) Icbc bank -0.2551* 0.1992* 0.0453* 0.9765* - (0.0289) (0.0175) (0.0130) (0.0049) Halkbank -3.5983* 0.2305* 0.0014 0.3349 -0.4401* (1.2547) (0.0754) (0.0477) (0.2404) (0.2045) Garanti -4.6179* 0.3437* -0.0424 0.2959 -0.3617* (1.2252) (0.0755) (0.0474) (0.1958) (0.1286) Denizbank -1.6904* 0.4005* 0.0434 0.6865* -0.1965* (0.1495) (0.0505) (0.0368) (0.0286) (0.0252 Akbank -0.1548* 0.0776* -0.0276*** 0.9842* -0.0125** (0.0475 (0.0179) (0.0165) (0.0066) (0.0058) Bankindex -4.4045* 0.3524* -0.0200 0.4060* -0.0716 (1.0739) (0.0696) (0.0427) (0.1527) (0.0696) N. of significant cases 11/11 10/11 5/11 9/11 8/11 Not: Numbers in parentheses indicate standard errors *, **,*** indicate the significance level at 1%, 5% and 10% respectively The estimate parameters from the variance equations (2) are presented in Table 4. The constant term (훼0), which is the time-independent component of volatility, is negative and statistically significant for all banks and bank portfolio index. The results also indicate that time-dependent components of volatility are as important as time-independent component of volatility since the ARCH (훼1) and GARCH(훽) coefficients are significant in most cases.

The ARCH term (훼1) is significant and positive for all cases except for Vakıfbank. Considering that 훼1 shows the impact of asymmetric past innovations on conditional variance (volatility), positive significant coefficients can be interpreted as the presence of volatility clustering. That is, the larger the absolute value of the standardized error, the higher the tendency of conditional volatility to rise.

Findings for 훼2 show that the ARCH parameters are statistically significant in 5/11 cases where only two of the significant parameters are attributed a negative sign. Positive and significant ARCH parameters indicate that past innovations have an asymmetric effect on current volatility. In other words, positive shocks or good news such as a market boom have a higher effect on volatility of returns than negative surprises like market stagnation. The results show that the ARCH estimates are negative and significant for Akbank and Vakıfbank, implying the presence of a leverage effect that negative shocks (unexpected bad news) increase predictable volatility of bank returns more than positive shocks. The ARCH estimates are non-significant in 6/11 cases, implying that the positive and negative surprises have a symmetric effect on the volatility of bank returns. The findings on the GARCH terms (훽) show that the GARCH coefficients are positive and less than one for all cases but they are significant in 9/11 cases. The GARCH parameters are also called persistence coefficients and they link current volatility to past volatility. Having persistence

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coefficient less than one (훽<1) is important for the stability of the system, implying that the persistence of volatility will die out over time. The results indicate that the significant persistence coefficients are very high for most of the banks ranging from 0.4060 to 0.9927. High values of persistence coefficients suggest volatility will remain high over several periods. Intuitively, corresponding values of persistence coefficients in weeks can be calculated by the half-life of a surprise formula ln(0.5)/ln(훽). The half-life formula measuring the duration of time in weeks required for half of the unit shocks to returns to die out. Using the measure of half-life, the highest persistence coefficient (0.9927) is for the Vakıfbank with average half-life of 94.6 weeks whereas the lowest persistence (0.6234) was for Qnbf bank with average half-life of approximately 1.47 weeks.

Furthermore, the 훼1 and 훽 coefficients are significant for almost all banks. This verifies that current conditional variance (volatility) is a function of past surprises and past volatility and is changing by time. In addition, the significant ARCH parameters are smaller in size than the significant GARCH parameters, implying that current volatility is more sensitive to old news (its own lagged values) than it is to the news about recent surprises in the market. The last column in Table 4 presents the estimation results related to the coefficient of 2008 financial crisis dummy variable. The findings indicate that the crisis dummy coefficients have statistically significant impact on volatility of returns for 8 banks. Among these significant coefficients, the sign of the crisis dummy coefficients is negative for seven banks and positive for Qnbf bank. While the positive significant coefficient indicates a rise of riskiness of Qnbff bank returns, the significant negative coefficients imply that the riskiness of these bank returns declined after the 2008 global financial crisis. In contrast to the results obtained for individual banks, it is worth mentioning that the crisis dummy coefficient is negative but non-significant for bank portfolio index, implying that global financial crisis has no effect on the riskiness of overall bank portfolio in Turkey. Considering the reform of the Turkish banking system following 2001 financial crisis, the findings about the crisis dummy can be solely attributed to the impact of globalization on risk. That is, financial globalization reduces the volatility of stock returns in tranquil times and it amplifies the volatility in periods of high uncertainty. 5. Conclusion This study empirically investigated the effects of bank risk factors (interest rate, exchange rate and credit risk) on bank returns for Turkish commercial banks listed in Borsa İstanbul. The relationship between bank returns and bank risk factors is first estimated using OLS. However, the results under the OLS model suffered from serious autocorrelation and heteroscedasticity. To cope with these problems and capture the time-varying nature of bank risk exposure factors, the AR(1)-EGARCH-M model was chosen as the most suitable model to fit the data in this study. The estimation results revealed a number of important findings about the relationship between bank risk factors and bank returns. First, the results clearly suggest that the aggregate level analysis with bank portfolio data cannot adequately accommodate the information on heterogeneities among individual banks in terms of responsiveness of bank returns to risk factors and in terms of the time-varying properties of volatility. Empirical findings show that banks differ from each other in terms of the type and magnitude of the risk they are exposed to, providing rich evidence for individual bank managers as well as policy makers. Second, the results indicate that although they differ in type and magnitude, the coefficients of risk exposure factors are negative and significant for more than half of the banks in the sample and bank portfolio. This implies that an increase in credit risk, the unexpected rise in exchange rates and interest rates lead to a fall in average bank returns. In other words, a significant amount of fluctuations in bank returns can be explained by fluctuations in bank risk factors. Third, empirical findings reveal that the nature of the relationship between volatility and bank returns differs significantly among banks and due to the level of analysis. For the bank portfolio 821

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and for the six banks in the sample, the risk-return parameters are statistically non-significant, indicating that increase in risk will not necessarily lead to an increase in the returns. However, the significant risk-return parameters are attributed negative signs for 2 banks and positive signs for the other 2. The positive risk-return parameters suggest that changes in the volatility of returns due to an increased risk will lead to fluctuations in bank returns, and vice versa. Fourth, the estimation results obtained from variance equation also provide ample evidence on the nature of volatility of bank returns. The examination of variance equation indicates that the time-independent (the constant term (훼0)) and time-dependent components (the ARCH(훼1) and the GARCH(훽) terms) of volatility are negative and statistically significant for almost all banks and bank portfolio index. This suggests that time-dependent components of volatility is as important as time-independent component of volatility in predicting current volatility.

Fifth, the ARCH (훼1) and the GARCH (훽) coefficients are significant for almost all banks, suggesting that current volatility is a function of past surprises and past volatility and is changing by time. In addition, the significant ARCH parameters are smaller in size than the significant GARCH parameters, which implies that current volatility is more sensitive to old news (its own lagged values) than it is to the news about recent surprises in the market. Sixth, the persistence coefficients (the GARCH terms (훽)), which link current volatility to past volatility, are positive and less than one for all cases, but they are significant only in 9/11 cases. The results indicate that the significant persistence coefficients are very high for most of the banks ranging from 0.4060 to 0.9927, implying that shocks to volatility will remain high over several periods.

Seventh, the findings for the ARCH parameters 훼2 are statistically significant in 5/11 cases where only two of the significant parameters are attributed a negative sign. Positive (negative) and significant ARCH parameters indicate that good news such as a market boom has a higher (lower) effect on volatility of returns than bad news like market stagnation does. Additionally, the ARCH (훼2) estimates are non-significant in 6/11 cases, implying that the positive and negative surprises have a symmetric effect on the volatility of bank returns. Eighth, the estimation results for the coefficient of crisis dummy variable, which takes one after 2008, indicate that crisis dummy coefficients are significant and negative (positive) for seven (Qnbf bank) banks, implying that the riskiness of these banks’ returns have declined (increased) after the 2008 global financial crisis. The crisis dummy coefficient is positive and significant only for Qnbf bank. In contrast to the results obtained for individual banks, it is worth mentioning that the crisis dummy coefficient is negative but non-significant for bank portfolio index implying that global financial crisis has no effect on the riskiness of overall bank portfolio in Turkey. In conclusion, empirical findings of this study show that bank level data has particular advantages over aggregate data in understanding the nature the relationship among bank risk factors, volatility, and bank returns. The results also indicate that the nature of this relationship has evidently changed as a result of changes in global financial markets. However, we need further evidence to support this argument. References Arabacı, H. (2018). “Türkiye’de bankacılık sektörünün gelişimi (2000-2016)”, Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi, 2 (3), 25-42. Bae, K. H., Chan, K., and A. Ng (2004). “Investibility and return volatility”, Journal of Financial Economics, 71, 239-63. Choi, J.J., Elyasiani, E., Kopecky, K.J. (1992). “The sensitivity of bank stock returns to market, interest and exchange rate risks”, Journal of Banking and Finance, 16, 982-1004.

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Araştırma Makalesi Assessing The Impact Of Bank Risk Factors On Turkish Bank’s Stock Returns Using The Egarch-M Model Banka Risk Faktörlerinin Türk Bankalarının Hisse Senedi Getirileri Üzerine Etkilerinin Garch-M Modeli İle Değerlendirilmesi İsmail Erkan ÇELİK Dr., Doğuş University, Faculty of Economics and Administrative Sciences, Department of Economics, Hasanpaşa Mah., Zeamet Sok. No:21, 34722 Acıbadem - Kadıköy/İstanbul [email protected] https://orcid.org/0000-0002-2274-0750 GENİŞLETİLMİŞ ÖZET Son yıllarda küresel ve ulusal piyasalarda meydana gelen değişimler, bu piyasalarda belirsizliğin önemli ölçüde artmasına yol açmıştır. Küresel piyasalarda artan belirsizliğin sebepleri olarak, Amerika Birleşik Devletlerinin başlattığı ticaret savaşları, küresel kriz sonrası dünya ekonomisindeki toparlanmanın çok yavaş ilerliyor olması, gelişmekte olan ülkelere olan finansal sermaye akımlarındaki azalma, gelişmiş ülkelerin uygulamaya koyduğu sıkı para politikaları sayılabilir. Piyasalardaki belirsizliği arttıran ulusal kaynaklı risk faktörlerinden bazıları da, artan enflasyon oranları, yeni hükümet sistemine ilişkin belirsizlikler, özel firmaların borçluluk oranlarının yüksek olması, büyüme hızındaki yavaşlama ve ödemeler bilançosu açıklarının sürdürülebilirliğine ilişkin kaygılardır. Belirsizliğin kaynakları ve etkilerinin anlaşılması çok önelidir çünkü küresel ve ulusal ekonomideki belirsizlik düzeylerindeki artış, bilhassa bankaların ve bankacılık sektörünün risk düzeyini arttırır, sırasıyla, banka iflaslarına ve ekonomik krize yol açma potansiyeline sahiptir. Artan belirsizlikle birlikte, bilhassa bankacılık sektörünü etkileme potansiyeli olan risk faktörlerinin neler olduğu, bu faktörlerin hangilerinin banka performanslarını nasıl ve ne yönde etkileyeceğine yönelik soruların sayısı da artmıştır. Bu soruların ampirik olarak belirlenmesi, banka yöneticileri, politika yapımcıları için olduğu kadar akademik çevreler açısından da önem taşımaktadır. Bir taraftan, bankacılık sektörünün risklere karşı iyi korunuyor olması, ekonomik istikrar ve sürdürülebilir büyüme hedefleri açısından hayati öneme sahip iken, diğer yandan, risk- getiri ilişkisi bankalar açısından da çok önemlidir. Çünkü risk faktörlerindeki ani artışlar banka iflaslarına yol açabilir. Bu çalışma, Türkiye için banka performanslarının banka risk faktörlerine olan duyarlılığını ampirik olarak analiz etmeyi amaçlamaktadır. Banka performans göstergesi olarak banka getiri oranları, bankacılık risk faktörleri olarak ta faiz oranı, döviz kuru ve kredi riski analize dâhil edilmiştir. Kredi riskini temsilen, sanayi indeksinde oransal değişme kullanılmıştır. Döviz kuru, dolar ve avro kurlarının eşit olarak ağırlıklandırılması ile oluşturulmuş kur değişkenidir. Faiz oranı, üç aylık mevduat faiz oranıdır. Döviz kuru ve faiz oranı risk değişkenleri ise, yukarıda tanımlanan döviz kuru ve faiz oranı değişkenlerinden elde edilen beklenmeyen döviz kuru ve faiz oranındaki değişmeler olarak tanımlanmıştır. Faiz ve döviz kurunun beklenmeyen değerleri, uygun ARIMA modellerinin hata terimler olarak oluşturulmuştur. En uygun model, döviz kuru için ARIMA(4,1,4), faiz oranı için ARIMA(9,1,4) olarak belirlenmiştir. Konuya ilişkin literatürde yer alan çalışmalar incelendiğinde, bu çalışmaların risk faktörlerinin hisse senedi getiri üzerindeki etkilerini belirlemek için genellikle en küçük kareler (EKK) yöntemini kullandığı görülmektedir. Bununla birlikte, EKK tahmin edicileri otokorelasyon ve değişen varyans durumunda taraflı ve tutarsız tahmin edicilerdir. Risk ve getiri oranı değişkenleri dikkate alındığında, bu değişkenlerin zaman içerisinde değişen bir özelliğe sahip olduğu, 825

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oynaklıklarının kümelendiği, varyansının zamanla değiştiği görülecektir. Bu nedenle, yakın tarihlerde yapılan ampirik çalışmalarda, banka getirileri ve risk faktörlerinin zamana göre değişen doğasını dikkate alan genelleştirilmiş otoregresif koşullu değişen varyans (GARCH) modelleri kullanılmaya başlanmıştır. Her ne kadar GARCH modelleri verilerdeki zamanla değişen dinamiklerini modellemekte başarı ise de, GARCH modelinde yer alan varyans denklemindeki katsayıların pozitif olması gerektiğine ilişkin varsayımı, çok kısıtlayıcı bulunmuştur. Öyle ki bu varsayımın, oynaklıktaki lineer-olmayan değişmeleri yakalamasına engel olduğu ileri sürülmüştür. Literatürde yer alan bütün bu tartışmaları dikkate alarak, bu çalışma faiz oranı ve döviz kurundaki beklenmeyen değişimin ve kredi riskinin banka getirileri üzerine etkilerini ampirik olarak tahmin etmekte daha uygun olan, otoregresif(1)-ortalamada genelleştirilmiş üssel otoregressif koşullu değişen varyans (AR(1)-EGARCH-M) modelini kullanmaktadır. EGARH modeli, GARCH modelinden farklı olarak, şokların koşullu varyans üzerine asimetrik etkilerinin modellenmesini mümkün kılmaktadır. Model iki kısımdan oluşmaktadır, ortalama ve varyans denklemleri. Ortalama denkleminde bağımlı değişken, banka getiri oranlar iken bağımsız değişkenler olarak, kredi, döviz kuru ve faiz oranı risk değişkenler, bağımlı değişkenin birinci gecikmesi ve şartlı varyansın logaritmik değeri yer almaktadır. Bu çalışmada, banka risk faktörlerinin banka getiri oranları üzerine etkileri incelenirken, bankalar arası farklılıkları dikkate alabilmek için, çalışmanın modeli, hem bireysel bankalar ve hem de banka portföyü için ayrı ayrı tahmin edilmiştir. Ayrıca, bu çalışmanın ampirik modeli, bankaların getirilerindeki oynaklıkların 2008 küresel finansal krizden sonra ortaya çıkan küresel ve yerel gelişmelere bağlı olarak değişip değişmediğini ölçen bir “global kriz kukla değişkeni” ile de genişletilmiştir. Kriz değişkeni, 2008 yılı öncesi için sıfır, 2008 sonrası için bir değerini alan kriz kukla değişkenidir. Kriz kukla değişkeni varyans denklemine eklenmiştir. Bu çalışmada, banka risk faktörlerinin (faiz oranı, faiz oranı ve kredi riski) Borsa İstanbul'da listelenen Türk ticari bankaların banka getirileri üzerindeki etkileri ampirik olarak incelenmiştir. Banka getirileri ve banka risk faktörleri arasındaki ilişki ilk olarak en küçük karele (EKK) tahmin yöntemi kullanılarak tahmin edilmiştir. EKK tahmin sonuçları incelendiğin, modelin hata terimlerinde ciddi otokorelasyon ve değişen varyans problemlerinin olduğu tespit edilmiştir. Bu sorunların üstesinden gelmek ve banka riski faktörlerinin zamana göre değişen özelliklerini modelleyebilmek için, bu çalışma da AR (1) -EGARCH-M modeli seçilmiştir. Modelin tahmininde kullanılan veriler, 1 Ocak 2002 - 4 Nisan 2019 dönemini kapsamakta, Borsa İstanbul'da listelenen 10 Türk ticari bankası ve banka portföy endeksine ait haftalık banka düzeyindeki verilerden oluşmaktadır. Modelin tahmin sonuçları, banka risk faktörleri ile banka getirileri arasındaki ilişki hakkında çok sayıda önemli bulgu ortaya koymaktadır: Birincisi, analiz sonuçlarına göre, banka portföy verileriyle toplulaştırılmış düzeyde yapılan analizinin, bankaların getiri oranlarının risk faktörlerine olan duyarlılığı ve riskin zamana bağlı olarak değişme özellikler açısından bireysel bankalar arasındaki farklılıklar hakkındaki bilgileri yeterince uygun hale getiremediğini göstermektedir. Ampirik bulgular, bankaların bireysel banka yöneticileri ve politika yapıcılar için zengin kanıtlar sağlayarak maruz kaldıkları riskin türü ve büyüklüğü bakımından birbirinden farklı olduğunu göstermektedir. İkincisi, risk faktörlerinin katsayılarının, farklı bankalar için farklı büyüklükte olmakla birlikte, banka portföyü ve örneklemde yer alan bankaların yarısından fazlası için negatif ve anlamlı olduğunu görülmektedir. Bu, kredi, döviz kurları ve faiz oranları risklerindeki bir artışın, ortalama olarak banka getirilerinde azalmaya yol açtığını göstermektedir. Diğer bir deyişle, banka getirilerindeki dalgalanmalar, önemli ölçüde banka risk faktörlerindeki dalgalanmalar ile açıklanabilir. Üçüncüsü, ampirik bulgular, oynaklık ve banka getirileri arasındaki ilişkinin niteliğinin, bankalar arasında ve analiz seviyesine bağlı olarak önemli ölçüde farklılık gösterdiğini ortaya koymaktadır. Banka portföyü ve örneklemdeki altı banka için, risk-getiri parametrelerinin istatistiki olarak 826

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anlamlı olmadığı bulunmuştur. Bu bulgu, riskteki artışın zorunlu olarak getirilerde bir artışa yol açmayacağı anlamına gelir. Bununla birlikte, risk-getiri parametreleri, 2 banka için anlamlı ve negatif ve diğer 2 için ise anlamlı ve pozitiftir. Anlamlı ve pozitif risk-getiri parametreler, artan risk nedeniyle getirinin oynaklığındaki değişikliklerin banka getirilerinde dalgalanmalara neden olacağı şeklinde yorumlanabilir. Benzer şekilde, anlamlı ve negatif risk-getiri parametreleri, artan risk bağlı olarak oynaklıkta görüle değişikliklerin getiri oranlarında azalamaya yol açacağı anlamına gelir. Dördüncüsü, varyans denkleminden elde edilen tahmin sonuçları, banka getirilerinin oynaklığının doğası hakkında da geniş kanıtlar sunmaktadır. Varyans denkleminin incelenmesi, oynaklığın zamandan bağımsız (sabit terim) ve zamana bağlı bileşenlerinin (ARCH ve GARCH terimleri) neredeyse tüm bankalar ve banka portföy indeksi için negatif ve istatistiksel olarak anlamlı olduğunu göstermektedir. Bu, oynaklığın zamana bağlı bileşeninin, cari oynaklığı tahmin etmede, oynaklığın zamandan bağımsız bileşeni kadar önemli olduğunu göstermektedir.

Beşinci olarak, ARCH (훼1) ve GARCH (훽) katsayıları hemen hemen tüm bankalar için istatistiki olarak anlamlıdır. Bu bulgu, cari oynaklığın geçmiş sürprizlerin ve geçmiş oynaklığın bir fonksiyonu olduğunu ve zamanla değiştiğini göstermektedir. Ek olarak, istatistiki olarak anlamlı ARCH katsayılarının, anlamlı GARCH katsayılarından büyük olması, cari oynaklığın eski haberlere (oynaklığın kendi gecikmeli değerleri) piyasadaki son sürprizlerle ilgili haberlerden daha duyarlı olduğunu ima etmektedir. Altıncı olarak, cari oynaklıkla geçmiş dönem oynaklığı arasındaki ilişkiyi gösteren devamlılık (persistence) katsayısı (GARCH (훽) terimi), bankaların tamamı ve banka portföy endeksi için pozitif ve birden küçüktür. Fakat bu katsayıların 9 tanesi anlamlı, 2 tanesi anlamsızdır. Çalışmanın bulgularına göre, devamlılık katsayıları, farklı bankalar için 0,4060-0,9927 arasında değişen değerler almaktadır. Bu anlamda, devamlılık katsayılarının oldukça yüksek olduğu ve haliyle oynaklık şoklarının birkaç dönem boyunca yüksek kalacağı söylenebilir.

Yedinci olarak, ARCH (훼2) katsayısı 5/11 durumda istatistiki olarak anlamlıdır. Bu durumların iki tanesinde katsayıların işareti negatif, üç durumda da pozitiftir. Pozitif (negatif) ve anlamlı ARCH katsayıları, piyasanın canlanmaması gibi iyi haberlerin, getirilerin oynaklığı üzerinde piyasanın durgunlaştığı gibi bir kötü haberlerden daha büyük (daha küçük) bir etki yaratacağı anlamına gelmektedir. Ayrıca, ARCH (훼2) katsayıları 6/11 durumda, istatistiki olarak anlamsız olduğu sonucuna ulaşılmıştır. Bu bulgu, pozitif ve negatif sürprizlerin banka getirilerinin oynaklığı üzerine etkilerinin simetrik olduğu anlamına gelmektedir. Sekizinci olarak, 2008 küresel finansal krizin oynaklık üzerine etkilerini incelemek üzere modele eklenen ve 2008 yılı öncesinde sıfır, 2008 yılı sonrasında bir değerini alan kriz kukla değişkeninin katsayısı, 7 banka için anlamlı ve negatif işarete, Qnbf bankası için anlamlı ve pozitif işarete sahiptir. Kriz kukla değişkeninin katsayısındaki negatif işaret, banka getirilerine ilişkin risklerin 2008 küresel finans krizi sonrasında azaldığı anlamına gelmektedir. Kriz kukla değişkeninin işareti sadece Qnbf bankası için pozitiftir. Bireysel bankalar için elde edilen sonuçların aksine, kriz kukla değişkenin katsayısı, banka portföy endeksi için negatif ve fakat istatistik olarak anlamsızdır. İlginç bir şekilde örneklemdeki bankaların büyük çoğunluğu için istatistiki olarak anlamlı olan kriz katsayısı, banka portföy endeksi için anlamsızdır. Bu bulgu, firma düzeyinde yapılan ampirik çalışmaların toplulaştırılmış verilerle yapılan çalışmalara göre daha zengin sonuçlar verdiğini göstermektedir. Birlikte ele alındığında, bu çalışmanın ampirik bulguları, banka düzeyindeki verilerin, banka risk faktörleri, oynaklık ve banka getirileri arasındaki ilişkinin doğasını anlamada toplulaştırılmış verilere göre çok daha fazla bilgi sunduğunu göstermektedir. Sonuçlar ayrıca, bu ilişkinin niteliğinin, küresel finansal piyasalardaki değişimler neticesinde açıkça değiştiğini göstermektedir. Ancak, bu argümanı desteklemek için daha fazla ampirik çalışma ve ampirik bulguya ihtiyaç vardır.

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