ASYMMETRIC LONG MEMORY PROPERTY IN VOLATILITY OF TURKEY STOCK MARKET

Serpil TÜRKYILMAZ, Mesut BALIBEY
368 178

Abstract


In this study, asymmetric long memory property in volatility of the Turkey Stock Market has been examined. For this purpose, the study contributes applications of long memory volatility models which take into account asymmetry property in finance literature. Firstly, FIGARCH model has been estimated to describe dynamics of symmetric long memory in volatility of Turkey Stock Market. Secondly, asymmetric response of volatility to good-bad news has been investigated by using EGARCH model. Finally, the presence of asymmetric volatility with the long memory process has been evaluated by using FIEGARCH model. The study provides important findings for investors and market participants. The results display long term persistence and the presence of asymmetric effects of shocks in volatility of Turkey Stock Market.


Full Text:

PDF (Türkçe)

References


(1) Engle, R.F. 1982. Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation, Econometrica. (50):987-1008.

(2) Bollerslev, T. 1986. Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics. (31):307-327.

(3) Nelson, D.B. 1991. Conditional Heteroskedas- ticity in asset returns: A new approach, Econometrica (59): 347-370.

(4) Engle, R.F. and Ng, V.K., 1993. Measuring and testing the impact of news on volatility. Journal of Finance. (48):1749-1777.

(5) Glosten, L.R., R. Jagannathan and Runkle D. 1993. On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance. (48):1779-1801.

(6) Zakoian, J.M. 1994. Threshold Heteroskedastic Models, Journal of Economic Dynamics and Control. (18):931-955.

(7) Granger, C.W.J. and R. Joyeux. 1980. An introduction to long-memory time series models and fractional differencing, Journal of Time Series Analysis. (1):15-39.

(8) Bollerslev, T. and Mikkelsen H.O. 1996. Modeling and Pricing Long-Memory in Stock

Market Volatility, Journal of Econometrics. (73), 1:151-184.

(9) Tse, Y. 1998. The Conditional Heteroskedasticity of the Yen-dollar Exchange Rate, Journal of Applied Econometrics. (193):49-55.

(10) Wright, J. 2002. Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns, Econometric Reviews, Taylor and Francis Journals. 21(4):397-417.

(11) Degiannakis, S. 2004. Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model, Appl. Financ. Econ. (14):1333–1342.

(12) Kilic, R. 2004. On the long memory properties of emerging capital markets: evidence from Istanbul stock exchange, Applied Financial Economics. (14): 915-922.

(13) Akgün, I. ve Sayyan, H. 2005. Forecasting Volatility in ISE-30 Stock Returns with Asymmetric Conditional Heteroscedasticity Models, Symposium of Traditional Finance, Marmara Üniversitesi, Bankacılık ve Sigortacılık Yüksekokulu, İstanbul, Türkiye, 127-141.

(14) Cavalcante, J. and Assaf, A. 2005. Long range dependence in the returns and volatility of the Brazilian stock market, European Review of Economics and Finance. (5):5–20.

(15) Bellalah, M., Aloui, C., and Abaoub, E. 2005. Long-range Dependence in Daily Volatility on Tunisian stock market. International Journal of Business. 10(3):191-216.

(16) Assaf, A. 2007. Dependence and mean reversion in stock prices: The case of the MENA region, Research in International Business and Finance. (20): 286–304.

(17) Kang, H.S. and Yoon, SM. 2007. Long memory properties in return and volatility: Evidence from the Korean stock market, Physica A. (385):591-600.

(18) Banerjee, A., and Sarkar, S. (2006). Modeling daily volatility of the Indian stock market using intra-day data. Working paper WPSNO.588. Indian Institute of Management.

(19) Goudarzi, H. 2010. Modeling Long term memory in the Indian Stock Market using Fractionally Integrated EGARCH model. International Journal of Trade, Economics and Finance, 1(3), 231-237.

(20) Demireli, E. 2010. Value at Risk (VAR) analysis and Long Memory: Evidence from FIAPARCH in Istanbul Stock Exchange, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 24, (4):217-228.

(21) Laurent, S., and J.P. Peters, 2001. G@RCH 2.0 :An Ox Package for Estimating and Forecasting Various ARCH Models, Proceedings 8th Forecasting Financial Markets. London, May 2001.

(22) Baillie, R.T., T.Bollerslev, and H.O. Mikkelsen. 1996. Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 74, (3):3–30.

(23) Bollerslev, T. and H.O.A. Mikkelsen. 1996. Modeling and Pricing Long-Memory in Stock Market Volatility. Journal of Econometrics 73:151-184.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.