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

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

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. Büberkökü, Ö. & Kızıldere, C. (2017, May). BIST100 endeksinin volatilite özelliklerinin incelenmesi. V. Anadolu International Conference in Economics, Eskişehir, Turkey. google scholar

9. 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
10. Çiçek, M. (2010). Türkiye’de faiz, döviz ve borsa: fiyat ve oynaklık yayılma etkileri. Ankara Üniversitesi SBF Dergisi, 65(2),1-28. google scholar
11. 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
12. Enders, W. (2004). Applied econometric time series (Second ed.). John Wiley, New York. google scholar
13. 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
14. 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
15. 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
16. Franses, P. H. & McAleer M. (2002). Financial volatility: an introduction. Journal of Applied Econometrics, 17, 419-424. google scholar
17. Garner, C. A. (1988). Has the stock market crash reduced consumer spending?. Federal Reserve Bank of Kansas City, 3-16. google scholar
18. 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
19. 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
20. 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
21. 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
22. Hong, Y. (2001). A Test for volatility spillover with application to exchange rates. Journal of Econometrics, 103, 183-224. google scholar
23. 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
24. Kanalıcı Akay, H. & Nargeleçekenler, M. (2006). Finansal piyasa volatilitesi ve ekonomi. Ankara Üniversitesi SBF Dergisi, 61 (4) , 5-36. google scholar
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. Özden, Ü. H. (2008). İMKB bileşik 100 endeksi getiri volatilitesinin analizi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 7(13), 339-350. google scholar
33. 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
34. 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
35. Taylor, S. J. (2005). Asset price dynamics, volatility, and prediction (STU-Student edition). Princeton University Press. google scholar
36. 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