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Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 5, September 2020, Pages: 185-200
Received: Aug. 8, 2020; Accepted: Aug. 24, 2020; Published: Sep. 14, 2020
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Mostafa Ahmed Aly, Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Ain Shams University, Cairo, Egypt
Ahmed Fathy Abd Elaal Elwaqdy, Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Ain Shams University, Cairo, Egypt
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This study evaluates the performance of a group of GARCH models under three different distributions in terms of their ability to estimate and forecasting the volatility of Egyptian Stock Exchange General Index (EGX30) in some horizon of forecasting using daily data for the period from January 2, 2000 to April 30, 2019, and tries to determine the best model according to some criteria. The primary purpose of the study is to investigate whether the two-regime MSW-GARCH model outperforms the uni-regime GARCH models in a very volatile time period during the global financial crisis. Hence, evaluating the predictive accuracy of the MSW-GARCH, and whether the MSW-GARCH assessed on the EGX30 would be successful. We explore and compare different possible sources of forecasts improvements: asymmetry in the conditional variance, fat-tailed distributions and regime-switching methodology. The results show that; there is an evidence that the EGX30 index has been affected by the crisis, and the TGARCH models are superior in predictive ability on EGX30 compared to the other tested models. Consequently, uni-regime GARCH models has priority in MSW-GARCH models in their forecasting performance. These models yield significantly better out-of-sample volatility forecasts.
"Volatility, Structural Changes, Uni-regime GARCH Models, Two-regime MSW-GARCH Models, and Egyptian Stock Market "
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Mostafa Ahmed Aly, Ahmed Fathy Abd Elaal Elwaqdy, Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices, American Journal of Theoretical and Applied Statistics. Vol. 9, No. 5, 2020, pp. 185-200. doi: 10.11648/j.ajtas.20200905.12
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ARNERIĆ, J., & ERJAVEC, N. (2010). Regime Switching Modelling of Structural Changes Caused by Financial Crisis. Univ. of Zagreb, Faculty of Economics and Business, 492-500.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327.
Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge Univ. Press, Univ. of Reading, the ICMA Centre.
Ding, Z., & Granger, C. (1996). Modeling volatility persistence of speculative returns: a new approach. Journal of Econometrics, 73, 185–215.
Ding, Z., Granger, C. W., & Engle, R. F. (1993). Along Memory Property of Stock Market Returns and A New Model. Journal of Empirical finance, 1 (1), 83-106.
Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007.
Engle, R. F., & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48 (5), 1749-1778.
EViews 8 User’s Guide (2013). Quantitative Micro Software, LLC.
Franses, P. H., & Dijk, D. v. (2003). Nonlinear Time Series Models in Empirical Finance. Cambridge: Cambridge University Press.
Franses, P. H., Dijk, D. v., & Opschoor, A. (2014). Time Series Models for Business and Economic Forecasting. Cambridge: Cambridge University Press.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48 (5), 1779-1801.
Gujarati, D. N. (2003). Basic Econometrics. West Point: United States Military Academy.
Haas, M., Mittnik, S., & Paolella, M. S. (2004). A New Approach to Markov-Switching GARCH Models. Journal of Financial Econometrics, 2 (4), 493-530.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series subject to changes in regime. Econometrica, 57, 357–384.
Henry, Ò. T. (2007). Between the Rock and a Hard Place: Regime Switching in the Relationship between Short-Term Interest Rates and Equity Returns in the UK. Univ. of Melbourne, Dept. of Economics, Research paper, 1019.
Kestel, S. (2013). The Time Series Analysis. Albert-Ludwigs Univ. Freiburg, Department of Empirical Research and Econometrics.
Klaassen, F. (1998). Improving GARCH volatility forecasts. Econometrics eJournal.
(2002). Improving GARCH volatility forecasts with regime switching GARCH. Empirical Economics, 27, 363–394.
Naeini, M. N., & Fatahi, S. (2012). Regime Switching GARCH Models and GARCH Models, in Stock Market of the Developing Countries. Razi Univ., Agricultural College, Master in economics.
Nelson, D. B. (1991). Conditional Heteroscedasticity in Asset Return: A New Approach. Econometrica, 59 (2), 347-370.
Pagan, A. R., & Schwert, G. W. (1990). Alternative Models for Conditional Stock Volatility. Journal of Econometrics, 45, 267–290.
Peters, J.-P. (2001). Estimating and Forecasting Volatility of Stock Indices Using Asymmetric GARCH Models and (Skewed) Student-t Densities. Univ. of Li`ege, Ecole d’Administration des Affaires, 31-54.
Priestley, M. B. (1980). State-dependent models: a general approach to non-linear time series analysis. Journal of Time Series Analysis, 1, 47–71.
Schwert, G. W. (1989). Tests for Unit Roots: A Monte Carlo Investigation. Journal of Business & Economic Statistics, 7 (2), 147–159.
Taylor, S. J. (1986). Modelling Financial Time Series. New York: John Wiley.
Tnders, W. (2015). Applied Econometric Time Series. Hoboken, NJ: Wiley: John Wiley & Sons.
Tsay, R. S., & Chen, R. (2019). Nonlinear Time Series Analysis. Hoboken, NJ: Wiley: Wiley Series in Probability and Statistics. John Wiley & Sons.
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