A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market

Yahaya Haruna Umar; Matthew Adeoye

doi:doi:10.11648/j.ajtas.20200904.11

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A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market

Yahaya Haruna Umar, Matthew Adeoye

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 4)

Received: 26 January 2020 Accepted: 7 April 2020 Published: 28 May 2020

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 9, Issue 4)
DOI	10.11648/j.ajtas.20200904.11
Page(s)	80-89
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

Markov Regime Switch, Stock Returns, Volatility Clustering, Financial Crisis

References

[1]	Benschop T. and Cabrera B. L., Volatility Modeling of CO2 EmissionAllowanceSpot Prices with Regime-Switching GARCH Models, 2014, pp1-11.
[2]	Cont, R. (2001). ”Empirical properties of asset returns: stylized facts andstatistical Issues”, Quantitative Finance, 1, p. 223-236.
[3]	Hamilton, J. D. (1989). “A new approach to the economic analysis ofnonstationarytime series and the business cycle” Econometrica, 57, p. 357–384.
[4]	Lindgren, G. (1978). “Markov Regime Models for Mixed Distributions andSwitching Regressions” Scandinavian Journal of Statistics, 5, p. 81-91.
[5]	Hamilton, J. D. (2005). “Regime-Switching Models” The New PalgraveDictionaryof Economics, Second Edition.
[6]	Bodie, Z., Kane, A., & Marcus, A. J. (1998). Essentials of Investment. NewYork: The Mcgraw-Hill Companies.
[7]	Central Bank of Nigeria statistical bulletin,(CBN, 2018).
[8]	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.
[9]	Brock, W. A., Hsieh, D. A. and LeBaron, B. (1991) Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence. MIT Press, Cambridge, London.
[10]	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:
[11]	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].
[12]	Armstrong, J. S., & Fildes, R. (1995). On the Selection of Error Measures for Comparisons Among Forecasting Methods. Retrieved from http://repository.upenn.edu/marketing_papers/63.
[13]	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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APA Style

Yahaya Haruna Umar, Matthew Adeoye. (2020). A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market. American Journal of Theoretical and Applied Statistics, 9(4), 80-89. https://doi.org/10.11648/j.ajtas.20200904.11

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Yahaya Haruna Umar; Matthew Adeoye. A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market. Am. J. Theor. Appl. Stat. 2020, 9(4), 80-89. doi: 10.11648/j.ajtas.20200904.11

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AMA Style

Yahaya Haruna Umar, Matthew Adeoye. A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market. Am J Theor Appl Stat. 2020;9(4):80-89. doi: 10.11648/j.ajtas.20200904.11

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@article{10.11648/j.ajtas.20200904.11,
  author = {Yahaya Haruna Umar and Matthew Adeoye},
  title = {A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {4},
  pages = {80-89},
  doi = {10.11648/j.ajtas.20200904.11},
  url = {https://doi.org/10.11648/j.ajtas.20200904.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200904.11},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market
AU  - Yahaya Haruna Umar
AU  - Matthew Adeoye
Y1  - 2020/05/28
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200904.11
DO  - 10.11648/j.ajtas.20200904.11
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  - 80
EP  - 89
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200904.11
AB  - 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.
VL  - 9
IS  - 4
ER  -

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Author Information

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

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APA Style

Yahaya Haruna Umar, Matthew Adeoye. (2020). A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market. American Journal of Theoretical and Applied Statistics, 9(4), 80-89. https://doi.org/10.11648/j.ajtas.20200904.11

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ACS Style

Yahaya Haruna Umar; Matthew Adeoye. A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market. Am. J. Theor. Appl. Stat. 2020, 9(4), 80-89. doi: 10.11648/j.ajtas.20200904.11

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AMA Style

Yahaya Haruna Umar, Matthew Adeoye. A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market. Am J Theor Appl Stat. 2020;9(4):80-89. doi: 10.11648/j.ajtas.20200904.11

Copy | Download

@article{10.11648/j.ajtas.20200904.11,
  author = {Yahaya Haruna Umar and Matthew Adeoye},
  title = {A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {4},
  pages = {80-89},
  doi = {10.11648/j.ajtas.20200904.11},
  url = {https://doi.org/10.11648/j.ajtas.20200904.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200904.11},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market
AU  - Yahaya Haruna Umar
AU  - Matthew Adeoye
Y1  - 2020/05/28
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200904.11
DO  - 10.11648/j.ajtas.20200904.11
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  - 80
EP  - 89
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200904.11
AB  - 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.
VL  - 9
IS  - 4
ER  -

Copy | Download