Markov Switching Autoregressive Model: A Volatility Model With Application to All Share Index Returns

  • E. M. Ikegwu Department of Statistics, Faculty of Science, University of Lagos, Nigeria; Department of Statistics, Yaba College of Technology, Yaba, Lagos, Nigeria.
  • J. N. Onyeka-Ubaka Department of Statistics, Faculty of Science, University of Lagos, Nigeria
Keywords: ASIR, Autoregressive, Markov Switching, Regimes, Transition probability, Volatility.

Abstract

This study explored the Autoregressive models incorporating Regime switching or Markov
switching non-linear predictive models. This stemmed from the complexities observed in economic
phenomena the understanding of which will help in recommending better model fits.
The study collected data on all share index returns (Jan, 1985 - December 2019) from the
Nigeria Stock Exchange, fit an appropriate MS-AR model and estimate its parameters. The
parameters of the model were obtained while their properties like the expected duration, autocorrelation
measure and the goodness of fit were equally computed in testing the applicability
of the model. The result shows that the MS (3)-AR (3) as a predictive model was appropriate,
efficient and robust enough for forecasting the returns of the all-share index of the Nigerian
stock exchange over the sampled period. The study is therefore relevant for modelling all share
index returns by investors, policy makers, researchers and the general public.

Published
2024-07-10
How to Cite
Ikegwu, E. M., & Onyeka-Ubaka, J. N. (2024). Markov Switching Autoregressive Model: A Volatility Model With Application to All Share Index Returns. International Journal of Mathematical Sciences and Optimization: Theory and Applications, 10(3), 95 - 105. Retrieved from http://ijmso.unilag.edu.ng/article/view/2199
Issue
Vol 10 No 3 (2024)
Section
Articles

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, adaptation, and reproduction in any medium, provided that the original work is properly cited.