MODELING SOUTH AFRICAN STOCK MARKET VOLATILITY USING UNIVARIATE SYMMETRIC AND ASYMMETRIC GARCH MODELS

Warren Rusere ,Forbes Kaseke

doi:10.46281/ijfb.v6i1.1177

Abstract

Contemporary empirical literature is rich in studies that have modelled and forecasted the nature and behavior of volatility of equity returns in both emerging and advanced stock markets. Modelling and estimating volatility is crucial in dynamic risk management, equity valuation and portfolio diversification. However, South African financial markets have not received ample attention in this regard. It is against this backdrop that we sought to determine the nature and behavior of volatility inherent in the South African stock market. Furthermore, we examined the effect of the 2014 global oil crisis on the volatility spillover in this market. The FTSE/JSE Top 40 index of the Johannesburg Stock Exchange has been selected as the study sample. Sample data for the period spans from October 14, 2009 to December 31, 2019, wherein the crisis period is from March 03, 2014 to February 27, 2015. Conditional volatility has been modelled and estimated using GARCH (1.1), GARCH-M (1.1), TGARCH (1.1) and EGARCH (1.1). The log likelihood, Akaike Information Criterion and Bayesian Information Criterion have been followed for model selection. The results showed that the EGARCH model is the most suitable for predicting the behavior of equity returns including for the global oil crisis period. JEL Classification Codes: C01, C13, C52, C53, C87.

Highlights

Investigating the nature and behavior of volatility exhibited by equity returns is of wider interest for researchers, market analysts, risk assessors and portfolio managers
Hinging on the empirical outcome of symmetric processes, the generalised autoregressive conditional heteroskedastic (GARCH) (1.1) model predicts the effect of average equity returns on the current equity better than the Generalized Autoregressive Conditional Heteroscedastic-in-Mean (GARCH-M) (1.1) model
Capturing and forecasting the nature and behavior of volatility exhibited by equity returns has received wide attention from researchers, market analysts and risk and portfolio managers

Summary

Introduction

Investigating the nature and behavior of volatility exhibited by equity returns is of wider interest for researchers, market analysts, risk assessors and portfolio managers. Financial management and investment decisions rely on modelling and estimating equity returns volatility as it aids in asset pricing strategies, risk management and portfolio optimisation (Abdalla & Winker, 2012). In an efficient stock market, fluctuations in equity return are used to estimate and predict the value of potential market and financial risk. Investment decisions and portfolio allocation are invariably futuristic, implying that the expected risks and expected returns must be accurately forecasted. By forecasting equity return volatility inherent on the FTSE/JSE Top 40 index, we establish an important relationship between current values and their expected future values.

Methods

Results

Conclusion