Analysis of Istanbul Stock Market Returns Volatility with ARCH and GARCH Models

Yıl 2024, , 37 - 58, 04.09.2024

İpek Yurttagüler

https://doi.org/10.26650/ISTJECON2023-1276992

Öz

In today’s world where globalization is intensely experienced, differences in risk perception, developments in capital markets, and the negativities faced in the markets due to uncertainty are very important when researching the structures of the stock markets, and therefore determining current volatilities. One of the biggest problems encountered is the inability to price stocks effectively. Therefore, estimating and modeling volatility becomes crucial. The diversity of the portfolio, created by international investors in the financial markets and the sustainability of their investment decisions, are closely related to the volatility variable. However, the fact that financial markets are more fragile in developing countries increases the importance of volatility. There are many different methods in the literature when estimating volatility. Due to the inadequacy of traditional time series models in estimating volatility, conditional heteroskedasticity models are used with ARCH and GARCH class models being frequently used. In this study, the series of daily opening values of the ISE100 Index covering from 02.01.2003 to 30.09.2022 was estimated using ARCH/GARCH models for volatility with the aim to determine which model has the higher explanatory power. According to the findings, the GARCH(1,1) model gave more meaningful results in explaining the ISE100 return volatility.

JEL Classification : E00 , C53 , D53

Anahtar Kelimeler

Volatility, Conditional Heteroskedasticity Models, Istanbul Stock Market

Kaynakça

• Al Najjar, D. (2016). Modelling and estimation of volatility using ARCH/GARCH models in Jordan’s stock market. Asian Journal of Finance & Accounting, 8, 152-167. https://doi.org/10.5296/ajfa.v8i1.9129 . google scholar
• Ali, F., Suri, P., Kaur, T. & Bisht D. (2022). Modelling time-varying volatility using GARCH models: evidence from the Indian stock market [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2022, 11:1098, 1-16. https://doi.org/10.12688/f1000research.124998.1 google scholar
• Atakan, T. (2009). Istanbul Menkul Kıymetler Borsasında değişkenliğin (volatilitenin) ARCH-GARCH yöntemleri ile modellenmesi. İstanbul Üniversitesi İşletme Fakültesi İşletme İktisadı Enstitüsü Yönetim Dergisi, 20(62), 48-61. google scholar
• Atıcı Ustalar, S. & Şanlısoy, S. (2021). COVID-19 Krizi’nin Türkiye ve G7 ülkelerinin borsa oynaklıkları üzerindeki etkisi. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 16(2), 446 - 462. https://doi. org/10.17153/oguiibf.884895 google scholar
• Ay, G., & Gün, M., (2020), Borsa İstanbul pay piyasasında volatilite modellemesi: BIST banka endeksi üzerine bir uygulama. BMIJ, 8(5), 3795-3814. http://dx.doi.org/10.15295/bmij.v8i5.1547 google scholar
• Baillie, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74, 3-30. https://doi. org/10.1016/S0304-4076(95)01749-6 google scholar
• Bollerslev, T. (1986). Generalized auto regressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1 google scholar
• Büberkökü, Ö. & Kızıldere, C. (2017, May). BIST100 endeksinin volatilite özelliklerinin incelenmesi. V. Anadolu International Conference in Economics, Eskişehir, Turkey. google scholar
• Çabuk, H. , Özmen, M. & Kökcen, A. (2011). Koşullu varyans modelleri: İMKB serileri üzerine bir uygulama. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(2), 1-18. https://doi. org/10.18603/std.01025 google scholar
• Çiçek, M. (2010). Türkiye’de faiz, döviz ve borsa: fiyat ve oynaklık yayılma etkileri. Ankara Üniversitesi SBF Dergisi, 65(2),1-28. google scholar
• Emeç, H. & Özdemir, M. O. (2014). Türkiye’de döviz kuru oynaklığının otoregresif koşullu değişen varyans modelleri ile incelenmesi. Finans Politik ve Ekonomik Yorumlar, (596) , 85-99 . google scholar
• Enders, W. (2004). Applied econometric time series (Second ed.). John Wiley, New York. google scholar
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. https://doi.org/10.2307/1912773 google scholar
• Engle, R. F., Lilien, D. M. & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica, 55(2), 391-407. https://doi.org/10.2307/1913242 google scholar
• Engle, R.F. & Patton, A. J.(2001). What good is a volatility model?. Quantitative Finance, 1, 237-245. google scholar Fabozzi, F. J.; Tunaru, R. & Wu, T. (2004). Modeling Volatility for Chinese Equity Markets. Annals of Economics and Finance, 5, 79-92. google scholar
• Franses, P. H. & McAleer M. (2002). Financial volatility: an introduction. Journal of Applied Econometrics, 17, 419-424. google scholar
• Garner, C. A. (1988). Has the stock market crash reduced consumer spending?. Federal Reserve Bank of Kansas City, 3-16. google scholar
• Goudarzi, H. & Ramanarayanan, C. S. (2010). Modelling and Estimation of Volatility in the Indian Stock Market. International Journal of Business and Management, 5(2), 85-98. Doi: 10.5539/ijbm. v5n2p85 google scholar
• Güriş, S. & Saçaklı Saçıldı, İ. (2011). İstanbul Menkul Kıymetler Borsası’nda hisse senedi getiri volatilitesinin klasik ve Bayesyen GARCH modelleri ile analizi. Trakya Üniversitesi Sosyal Bilimler Dergisi, 13 (2) , 153-171. google scholar
• Gürsakal, S. (2009). Varyans kırılması gözlemlenen serilerde garch modelleri: döviz kuru oynaklığı örneği. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 0 (32) , 319-337. google scholar
• Güzel F. & Acar M., (2021, May). The effects of epidemics on capital markets volatility: A case study of Borsa Istanbul. CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13(1), 50-70. google scholar
• Hong, Y. (2001). A Test for volatility spillover with application to exchange rates. Journal of Econometrics, 103, 183-224. google scholar
• Kalaycı, Ş. (2005). Borsa ve ekonomide volatilite ilişkisi: İMKB’de bir şartlı varyans analizi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10 (1) , 241-250 . google scholar
• Kanalıcı Akay, H. & Nargeleçekenler, M. (2006). Finansal piyasa volatilitesi ve ekonomi. Ankara Üniversitesi SBF Dergisi, 61 (4) , 5-36. google scholar
• Karabacak, M. , Meçik, O. & Genç, E. (2014). Koşullu değişen varyans modelleri ile BİST 100 endeks getirisi ve altın getiri serisi volatilitesinin tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 6 (1) , 79-90. google scholar
• Koy, A. & Ekim Dertli, S. (2016). Borsa İstanbul sektör endekslerinin volatilite modellemesi. Trakya Üniversitesi İktisadi ve İdari Bilimler Fakültesi E-Dergi, 5 (2) , 1-23. google scholar
• Kuzu, S. (2018). Borsa İstanbul Endeksi (BİST 100) getiri volatiletesinin arch ve garch modeli ile tahmin edilmesi. Journal of Accounting and Taxation Studies, Special Issue of the 10th Year, 608624. https://doi.org/10.29067/muvu.384418 google scholar
• Nargeleçekenler, M. (2011). Euro kuru satış değerindeki volatilitenin arch ve garch modelleri ile tahmini. İstanbul Üniversitesi İktisat Fakültesi Mecmuası, 54 (2), 153-179. google scholar
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260 google scholar
• Nelson, D. B. (1996). Modelling stock market volatility change. Rossi, P. E. (ed.), Modelling Stock Market Volatility (p. 3-15). Academic Press, London. google scholar
• Öner,S. & Öner,H.(2023).Symmetric and asymmetric volatility: Forecasting the Borsa Istanbul 100 index return volatility. Financial Internet Quarterly,19(1), 48-56. https://doi.org/10.2478/fiqf-2023-0005 google scholar
• Özden, Ü. H. (2008). İMKB bileşik 100 endeksi getiri volatilitesinin analizi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 7(13), 339-350. google scholar
• Songül, H. (2010). Otoregresif koşullu değişen varyans modelleri: döviz kurları üzerine uygulama. Uzmanlık Yeterlilik Tezi. Türkiye Cumhuriyet Merkez Bankası Araştırma ve Para Politikası Genel Müdürlüğü, Ankara. google scholar
• Taştan, H. & Güngör, A., (2019), Türkiye hisse senedi piyasa volatilitesinin makroekonomik temelleri. Business and Economics Research Journal, Vol. 10, No. 4, 823-832. Doi: 10.20409/berj.2019.203 google scholar
• Taylor, S. J. (2005). Asset price dynamics, volatility, and prediction (STU-Student edition). Princeton University Press. google scholar
• Uğurlu, E., Thalassinos E. & Muratoğlu Y., (2014). Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey, International Journal of Economics & Business Administration (IJEBA), 2 (3), 72-87. DOI: 10.35808/ijeba/49 google scholar