Volume 9, Issue 4, July 2020, Page: 80-89
A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market
Yahaya Haruna Umar, Statistics Department, Faculty of Science, University of Abuja, Abuja, Nigeria
Matthew Adeoye, Statistics Department, Faculty of Science, University of Abuja, Abuja, Nigeria
Received: Jan. 26, 2020;       Accepted: Apr. 7, 2020;       Published: May 28, 2020
DOI: 10.11648/j.ajtas.20200904.11      View  217      Downloads  107
Understanding and forecasting the behavior of volatility in stock market has received significant attention among researchers and analysts in the last few decades due to its crucial roles in financial markets. Portfolios managers, option traders, and market makers are all interested in the possibility of forecasting, with a reasonable level of accuracy. This study examined the volatility on the Nigeria stock market by comparing two Markov regime switching Autoregressive (MS-AR) Models estimated at different lagged values using the Nigeria stock exchange monthly All Share Index data from 1988 to 2018 in the Central Bank of Nigeria (CBN) Statistical Bulletin. It was found that factors like financial crisis, information flow, trading volume, economical aspects and investor’s behavior are the causes of volatility in the stock market. The results and forecasts obtained from the statistical analysis in this research showed that the stock market will experience a steady growth in 2020 and beyond. Also, the stock market is experiencing fluctuations in the price indices which show that over the years, investors have been exposed to some certain risks in the time past. We therefore recommended that researchers should focus more attention in developing robust statistical model that will reflect and continue to monitor future trends and realities.
Markov Regime Switch, Stock Returns, Volatility Clustering, Financial Crisis
To cite this article
Yahaya Haruna Umar, Matthew Adeoye, A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market, American Journal of Theoretical and Applied Statistics. Vol. 9, No. 4, 2020, pp. 80-89. doi: 10.11648/j.ajtas.20200904.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Benschop T. and Cabrera B. L., Volatility Modeling of CO2 EmissionAllowanceSpot Prices with Regime-Switching GARCH Models, 2014, pp1-11.
Cont, R. (2001). ”Empirical properties of asset returns: stylized facts andstatistical Issues”, Quantitative Finance, 1, p. 223-236.
Hamilton, J. D. (1989). “A new approach to the economic analysis ofnonstationarytime series and the business cycle” Econometrica, 57, p. 357–384.
Lindgren, G. (1978). “Markov Regime Models for Mixed Distributions andSwitching Regressions” Scandinavian Journal of Statistics, 5, p. 81-91.
Hamilton, J. D. (2005). “Regime-Switching Models” The New PalgraveDictionaryof Economics, Second Edition.
Bodie, Z., Kane, A., & Marcus, A. J. (1998). Essentials of Investment. NewYork: The Mcgraw-Hill Companies.
Central Bank of Nigeria statistical bulletin,(CBN, 2018).
Brock, W. A., Dechert, W. D., & Scheinkman, J. A. (1987). A Test forIndependence Based on The Correlation Dimension. Department ofEconomics, University of Wisconsin at MadisonUniversity of Houston, andUniversity of Chicago.
Brock, W. A., Hsieh, D. A. and LeBaron, B. (1991) Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence. MIT Press, Cambridge, London.
Akaike, H. (1973), "Information theory and an extension of the maximum likelihood principle", in Petrov, B. N.; Csáki, F. (eds.), 2nd International Symposium on Information Theory, Tsahkadsor, Armenia, USSR, September 2-8, 1971, Budapest:
Decision 411 forecasting, 2010, What’s the bottom line? How to compare models, Availableat: file:///D:/Informatics/thesis/in%20KTM/error%20metrics/How%20to%20compare%20models.htm, [Accessed: 23 jan 2011].
Armstrong, J. S., & Fildes, R. (1995). On the Selection of Error Measures for Comparisons Among Forecasting Methods. Retrieved from
Armstrong, J. S. (2001), “Role-playing: A method to forecast decisions,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Press.
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