Borsa Endeks Getirilerinde İkili Uzun Hafıza Analizi

Bu çalışmanın amacı, zayıf formda etkin piyasa hipotezi bağlamında birleşik ARFIMA-FIGARCH modeli ve yapısal kırılma testi kullanarak beş farklı borsa endeks getiri serisi için ikili uzun hafıza özelliklerini incelemektir. Modeller S&P500, FTSE100, DAX, CAC40 ve ISE100 borsa endekslerinin günlük kapanış fiyatları kullanılarak test edilmiştir. Volatilite sürekliliği üzerinde yapısal kırılmaların etkilerini belirlemek üzere ICSS (Iterative Cumulative Sums of Squares) algoritması ile varyanstaki kırılmalar tespit edilmiş ve modellere kukla değişkenler olarak eklenmiştir. Analiz sonuçlarına göre, tüm borsalar için ikili uzun hafızanın bulunduğu anlaşılmıştır. Ayrıca volatilitenin öngörülebilir yapı göstermesi nedeniyle tüm borsaların zayıf formda etkinsiz oldukları sonucuna varılmıştır. Bunun yanı sıra, varyanstaki yapısal kırılmaların modellere eklenmesiyle volatilite dinamiklerinin daha doğru hesaplandığı ve volatilite sürekliliğinin fiilen azaldığı saptanmıştır

Analyzing the Dual Long Memory in Stock Market Returns

The purpose of this study is to examine the dual long memory properties for five stock market returns by using joint ARFIMAFIGARCH model and structural break test in context of weak form efficient market hypothesis. The models are estimated by using daily closing prices for S&P500, FTSE100, DAX, CAC40 and ISE100. In an effort to assess the impact of structural breaks in volatility persistence, the breaks in variance are detected by using the Iterated Cumulative Sums of Squares (ICSS) algorithm, and dummy variables are incorporated to the models. Empirical findings show that the dual long memory exists for all stock markets. Also the volatility has a predictable structure and indicates that all stock markets are weak form inefficient. Further, it is found that incorporating information on structural breaks in variance improves the accuracy of estimating volatility dynamics and effectively reduces the persistence of volatility

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  • Aggarwal, R., Inclan, C. and Leal, R. (1999) “Volatility in Emerging Stock Markets” Journal of Financial and Quantitative Analysis, 34:33-55.
  • Ané, T. (2006) “An Analysis of the Flexibility of Asymmet- ric Power GARCH Models” Computational Statistics and Data Analysis, 51:1293-1311.
  • Baillie, R.T., Bollerslev, T. and Mikkelsen, H.O. (1996) “Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity” Journal of Econometrics, 74:3–30.
  • Barkoulas, J.T., Baum, C.F., Travlos, N. (2000) “Long Memory in the Greek Stock Market” Applied Financial Econom- ics, 10:177–184.
  • Bollerslev, T. (1987) “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return” Review of Economics and Statistics, 69(3):542-547.
  • Cheong, C.W., Isa, Z. and Nor, A.H.S.M. (2008) “Frac- tionally Integrated Time-varying Volatility under Structural Break: Evidence from Kuala Lumpur Composite Index” Sains Malaysiana, 37(4):405-411.
  • Cheung, Y.W. (1993) “Tests for Fractional Integration: A Monte Carlo Investigation” Journal of Time Series Analysis, 14(4):331-345.
  • Choi, K. and Zivot, E. (2007) “Long Memory and Struc- tural Changes in the Forward Discount: An Empirical Investiga- tion” Journal of International Money and Finance, 26:342-363.
  • Ding, Z., Granger, C.W.J. and Engle, R.F. (1993) “A Long Memory Property of Stock Market Returns and A New Model” Journal of Empirical Finance, 1:83–106.
  • Engle, R.F. (1982) “Autoregressive Conditional Heterosce- dasticity with Estimates of The Variance of United Kingdom Inflation” Econometrica, 50(4):987-1007.
  • Eoma, C., Choi, S., Oh, G. and Jung, W.S. (2008) “Hurst Exponent and Prediction Based on Weak-form Efficient Market Hypothesis of Stock Markets” Physica A, 387:4630–4636.
  • Fama, E.F. (1970) “Efficient Capital Markets: A Review of Theory and Empirical Work” Journal of Finance, 25:383–417.
  • Granger, C. and Joyeux, R. (1980) “An Introduction to Long Memory Time Series Models and Fractional Differencing” Journal of Time Series Analysis, 1:15–39.
  • Härdle, W.K. and Mungo, J. (2008) “Value-at-Risk and Ex- pected Shortfall When There Is Long Range Dependence” SFB 649 ‘Economic Risk’ Discussion Paper, 6:1-39.
  • Hosking, J.R.M. (1981) “Fractional Differencing” Biometri- ka, 68: 165-176.
  • Inclan, C. and Tiao, G.C. (1994) “Use of Cumulative Sums of Squares for Retrospective Detection of Changes in Variance” Journal of the American Statistic Association, 89:913–923.
  • Jorion, P. (2007) Value at Risk: The New Benchmark for Man- aging Financial Risk, 3rd Edition, New York, McGraw Hill Inc.
  • Kang, S.H. and Yoon, S.M. (2007) “Long Memory Proper- ties in Return and Volatility: Evidence from the Korean Stock Market” Physica A, 385:591-600.
  • Kang, S.H., Chob, H.G. and Yoon, S.M. (2009) “Modeling Sudden Volatility Changes: Evidence from Japanese and Korean Stock Markets” Physica A, 388:3543-3550.
  • Karanasos, M. and Kartsaklas, A. (2009) “Dual Long-Mem- ory, Structural Breaks and The Link Between Turnover and The Range-Based Volatility” Journal of Empirical Finance, 16(5):838- 851.
  • Kasman, A. (2009) “The Impact of Sudden Changes on the Persistence of Volatility: Evidence from The BRIC Countries” Applied Economics Letters, 16:759-764.
  • Kasman, A., Kasman, S. and Torun, E. (2009) “Dual Long Memory Property in Returns and Volatility: Evidence from the CEE Countries’ Stock Markets” Emerging Markets Review, 10: 122-139.
  • Kasman, A. and Torun, E. (2007) “Long Memory in the Turkish Stock Market Return and Volatility” Central Bank Re- view, 2:13-27.
  • Korkmaz, T., Çevik, E.İ. and Özataç, N. (2009) “Testing for Long Memory in ISE Using ARFIMA-FIGARCH Model and Structural Break Test” International Research Journal of Finance and Economics, 26:1450-2887.
  • Kupiec, P.H. (1995) “Techniques for Verifying the Accuracy of Risk Measurement Models” Journal of Derivatives, 3:73–84.
  • Lambert, P. and Laurent, S. (2001) “Modelling Finan- cial Time Series Using GARCH-Type Models with a Skewed Student Distribution for The Innovations” Discussion Paper, No:0125
  • Malik, F. and Hassan, S.A. (2004) “Modeling Volatility in Sector Index Returns with GARCH Models Using An Iterated Algorithm” Journal of Economics and Finance, 28(2): 211-225.
  • Mun, H.W., Sundaram, L. and Yin, O.S. (2008) “Lever- age Effect and Market Efficiency of Kuala Lumpur Composite Index” International Journal of Business and Management, 3(4): 138-144.
  • Pooter, M. and Dijk, D.V. (2004) “Testing for Changes in Volatility in Heteroskedastic Time Series – A Further Examina- tion” Econometric Institute Report EI 2004-38:1-39.
  • Tang, T. and Shieh, S.J. (2006) “Long Memory in Stock Index Futures Markets: A Value-at-Risk Approach” Physica A, 366:437-448.
  • Vougas, D. (2004) “Analysing Long Memory and Volatil- ity of Returns in the Athens Stock Exchange” Applied Financial Economics, 14:457-460.