Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices

Mostafa Ahmed Aly; Ahmed Fathy Abd Elaal Elwaqdy

doi:doi:10.11648/j.ajtas.20200905.12

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Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices

Mostafa Ahmed Aly, Ahmed Fathy Abd Elaal Elwaqdy

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 5)

Received: 8 August 2020 Accepted: 24 August 2020 Published: 14 September 2020

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 9, Issue 5)
DOI	10.11648/j.ajtas.20200905.12
Page(s)	185-200
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

"Volatility, Structural Changes, Uni-regime GARCH Models, Two-regime MSW-GARCH Models, and Egyptian Stock Market "

References

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APA Style

Mostafa Ahmed Aly, Ahmed Fathy Abd Elaal Elwaqdy. (2020). 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, 9(5), 185-200. https://doi.org/10.11648/j.ajtas.20200905.12

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ACS Style

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. Am. J. Theor. Appl. Stat. 2020, 9(5), 185-200. doi: 10.11648/j.ajtas.20200905.12

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AMA Style

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. Am J Theor Appl Stat. 2020;9(5):185-200. doi: 10.11648/j.ajtas.20200905.12

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@article{10.11648/j.ajtas.20200905.12,
  author = {Mostafa Ahmed Aly and Ahmed Fathy Abd Elaal Elwaqdy},
  title = {Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {5},
  pages = {185-200},
  doi = {10.11648/j.ajtas.20200905.12},
  url = {https://doi.org/10.11648/j.ajtas.20200905.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200905.12},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices
AU  - Mostafa Ahmed Aly
AU  - Ahmed Fathy Abd Elaal Elwaqdy
Y1  - 2020/09/14
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200905.12
DO  - 10.11648/j.ajtas.20200905.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 185
EP  - 200
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200905.12
AB  - 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.
VL  - 9
IS  - 5
ER  -

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Author Information

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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APA Style

Mostafa Ahmed Aly, Ahmed Fathy Abd Elaal Elwaqdy. (2020). 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, 9(5), 185-200. https://doi.org/10.11648/j.ajtas.20200905.12

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ACS Style

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. Am. J. Theor. Appl. Stat. 2020, 9(5), 185-200. doi: 10.11648/j.ajtas.20200905.12

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AMA Style

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. Am J Theor Appl Stat. 2020;9(5):185-200. doi: 10.11648/j.ajtas.20200905.12

Copy | Download

@article{10.11648/j.ajtas.20200905.12,
  author = {Mostafa Ahmed Aly and Ahmed Fathy Abd Elaal Elwaqdy},
  title = {Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {5},
  pages = {185-200},
  doi = {10.11648/j.ajtas.20200905.12},
  url = {https://doi.org/10.11648/j.ajtas.20200905.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200905.12},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Suggested Statistical Models for Analysis Non-stationary Time Series and Sudden Changes with Application on Stock Exchange Indices
AU  - Mostafa Ahmed Aly
AU  - Ahmed Fathy Abd Elaal Elwaqdy
Y1  - 2020/09/14
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200905.12
DO  - 10.11648/j.ajtas.20200905.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 185
EP  - 200
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200905.12
AB  - 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.
VL  - 9
IS  - 5
ER  -

Copy | Download