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

Yahaya Haruna Umar,Matthew Adeoye

doi: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.

Highlights

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
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
This study examined the volatility on the Nigeria stock market by comparing two Markov regime switching Autoregressive (MS-Autoregressive Process (AR)) Models estimated at different lagged values using the Nigeria stock exchange monthly All Share Index data from 1988 to 2018

Summary

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. A financial market often changes patterns over time. 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. 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

Objectives

Methods

Findings

Conclusion