DAILY VOLATILITY ANALYSIS OF BIST 100 CONSTITUENTS BETWEEN 2018-2020

Year 2020, , 340 - 360, 31.12.2020

Cavit Pakel Kadir Özen

https://doi.org/10.14780/muiibd.854509

Cited By: 1

Abstract

The Turkish economy has experienced two important shocks in the recent past. The first is a currency shock
which occurred in August 2018. A second, substantially more impactful, shock is the COVID-19 pandemic,
which began in early 2020 and is still in progress. An interesting question from the perspectives of both
policy makers and practitioners is whether significant changes in key economic and financial variables
have been observed in the period marked by these two shocks. We investigate this question for the volatility
of the daily returns on BIST 100 constituent equities, using a novel panel GARCH modelling approach.
We find that during the periods associated with the two shocks, the stock market volatility has increase
substantially. Importantly, this increase has been greater and more persistent during the pandemic period.
Moreover, our analysis of sector-specific volatilities also reveals that this period of two shocks has witnessed
a uniform increase in the average volatilities of all sectors, compared to the period before.

Keywords

BIST 100, COVID-19, GARCH, financial volatility

References

• ALPER, C. E., Fendoglu, S., Saltoglu, B. (2012). MIDAS Volatility Forecast Performance under Market Stress: Evidence from Emerging Stock Markets, Economics Letters, 117: 528-532.
• ARELLANO, M., Bonhomme, S. (2009). Robust Priors in Nonlinear Panel Data Models, Econometrica, 77: 489-536.
• BATCHELOR, R., Orakcioglu, I. (2003). Event-related GARCH: The Impact of Stock Dividends in Turkey, Applied Financial Economics, 13: 295-307.
• BİLDİK, R., Elekdag, S. (2004). Effects of Price Limits on Volatility: Evidence from the Istanbul Stock Exchange, Emerging Markets Finance and Trade, 40: 5-34.
• BOLLERSLEV, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 51: 307-327.
• BOLLERSLEV, T. (2010). Glossary to ARCH (GARCH*). T. Bollerslev, J. Russell, and M. Watson (Eds.), Volatility and Time Series Econometrics: Essays in Honor of Robert Engle, Oxford University Press, 137-163.
• BOLLERSLEV, T., Wooldridge, J. M. (1992). Quasi-maximum Likelihood Estimation and Inference in Dynamic Models with Time-varying Covariances, Econometric Reviews, 11: 143-172.
• BROWNLEES, C. T. (2019). Hierarchical GARCH, Journal of Empirical Finance, 51: 17-27.
• CAKAN, E., Doytch, N., Upadhyaya, K. P. (2015). Does U.S. Macroeconomic News Make Emerging Financial Markets Riskier? Borsa Istanbul Review, 15: 37-43.
• ENGLE, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation, Econometrica, 50: 987-1008.
• ERDEM, C., Arslan, C. K., Erdem, M. S. (2005). Effects of Macroeconomic Variables on Istanbul Stock Exchange Indexes, Applied Financial Economics, 15: 987-994.
• FERNÁNDEZ-VAL, I., Weidner, M. (2018). Fixed Effects Estimation of Large-T Panel Data Models, Annual Review of Economics, 10: 109-138.
• FRANCQ, C., Horváth, L., Zakoïan, J. M. (2011). Merits and Drawbacks of Variance Targeting in GARCH Models, Journal of Financial Econometrics, 9: 619-656.
• FRANCQ, C., Zakoïan, J. M. (2010). GARCH Models: Structure, Statistical Inference and Financial Applications, Wiley.
• FREES, E. W. (1995). Assessing Cross-Sectional Correlation in Panel Data, Journal of Econometrics, 69: 393-414.
• FRIEDMAN, M. (1937). The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance, Journal of the American Statistical Association, 32: 675-701.
• GULAY, E., Emec, H. (2018). Comparison of Forecasting Performances: Does Normalization and Variance Stabilization Method Beat GARCH(1,1)-type Models? Empirical Evidence from the Stock Markets, Journal of Forecasting, 37: 133-150.
• KAYRAL, İ. E., Tandoğan, N. Ş. (2020). BİST100, Döviz Kurları ve Altının Getiri ve Volatilitesinde COVID-19 Etkisi, Gaziantep University Journal of Social Sciences, 19: 687-701.
• KELEŞ, E. (2020). COVID-19 ve BİST-30 Endeksi Üzerine Kısa Dönemli Etkileri, Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 42: 91-105.
• KILIÇ, R. (2004). On the long Memory Properties of Emerging Capital Markets: Evidence from Istanbul Stock Exchange, Applied Financial Economics, 14: 915-922.
• KILIÇ, Y. (2020). Borsa İstanbul’da COVID-19 (Koronavirüs) Etkisi, Journal of Emerging Economies and Policy, 5: 66-77.
• KÖKSAL, B. (2009). A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns, Journal of Economic and Social Research, 11: 1-29.
• ÖZDEMİR, L. (2020). COVID-19 Pandemisinin BIST Sektör Endeksleri Üzerine Asimetrik Etkisi, Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 5: 546-556.
• ÖZKAN, O. (2020). Volatility Jump: The Effect of COVID-19 on Turkey Stock Market, Gaziantep University Journal of Social Sciences, 19: 386-397.
• ÖZTÜRK, Ö., Şişman, M. Y., Uslu, H., Çıtak, F. (2020). Effects of COVID-19 Outbreak on Turkish Stock Market: A Sectoral-Level Analysis, Hitit University Journal of Social Sciences Institute, 13: 56-68.
• PAKEL, C. (2019). Supplementary Appendix for Bias Reduction in Nonlinear and Dynamic Panels in the Presence of Cross-section Dependence, unpublished manuscript, 1-80.
• PAKEL, C., Shephard, N., Sheppard, K. (2011). Nuisance Parameters, Composite Likelihoods and a Panel of
• GARCH Models, Statistica Sinica, 21: 307-329.
• PESARAN, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels, IZA Discussion Paper Series, No 1240: 1-39.
• PESARAN, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence, Journal of Applied Econometrics, 22: 265-312.
• SEVÜTEKİN, M., Nargeleçekenler, M. (2004). İstanbul Menkul Kıymetler Borsasında Getiri Volatilitesinin Modellenmesi ve Önraporlanması, Ankara Üniversitesi SBF Dergisi, 61: 243-265.
• SHEPPARD, K. (2020). Financial Econometrics Notes, https://www.kevinsheppard.com/files/teaching/mfe/notes/financial-econometrics-2020-2021.pdf, (Last accessed: 16.11.2020).