American Journal of Theoretical and Applied Statistics 2020; 9(4): 80-89 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20200904.11 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online) A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market Yahaya Haruna Umar, Matthew Adeoye Statistics Department, Faculty of Science, University of Abuja, Abuja, Nigeria Email address: 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 Received: January 26, 2020; Accepted: April 7, 2020; Published: May 28, 2020 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. Keywords: Markov Regime Switch, Stock Returns, Volatility Clustering, Financial Crisis 1. Introduction The importance of 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. Volatility prediction is a critical task in asset valuation and risk management for investors and financial intermediaries. The price of almost every derivative security is affected by swings in volatility. A financial market often changes patterns over time. It can also exhibit dramatic changes due to unexpected events such as natural hazards and financial crisis. A widely accepted fact is that financial markets behave quite differently in different economic situations. Traders often adjust their portfolios according to market trend, which is defined as the long term tendency of a financial market to move in a certain direction. A financial market is traditionally classified into 3 categories: bearish, bullish and neutral. The first two terms describe overall market gain and loss respectively. The term neutral market is used when no strong upward or downward trend is observed. Several benefits can be derived from generating accurate forecast of volatility. Volatility forecast is a key indicator in assessing the performance of the stock market in order for both indigenous and foreign speculators to make accurate speculations and decisions on investments. The issue with volatility of stock market price refers to the fluctuations that may be observed in stock market prices over time. The major reason for the ups and downs in the stock market may be traced to macroeconomic instability. Since the stock market operate in a macroeconomic environment, it is therefore necessary that the environment must be an enabling one in order to realize its full potentials. The problem with forecasting the stock market price is that the return distribution can change considerably over time. Volatility is an extremely complex thing to forecast because of the inherent instability of the variable (variability of the random). Volatility forecast sometimes may be uncertain, since it is just a mere projection based on some econometric technique in most cases.