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
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.
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.
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