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 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 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 banks’ 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 Çelik, İ. E. 54(2) 2019 811-827 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 Turkey. 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 Çelik, İ. E. 54(2) 2019 811-827 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