Modeling Stock Market Volatility Using GARCH Approach on the Ghana Stock Exchange

Abstract

The study examined and modeled stock market volatility of financial return series for three listed equities on the Ghana Stock Exchange (GSE). A historical data from 25<sup>th</sup> June 2007 to 31<sup>st</sup> October 2014 was considered for the analysis. The series for each of the three equities were tested for stationarity using the KPSS test. Series found to be non-stationary were transformed to be stationary. The study fitted a GARCH (p, q) model for volatility. GARCH (1, 1), GARCH (1, 2), GARCH (2, 1) and the GARCH (2, 2) models were fitted to residual series of some three equities. Results revealed the presence of volatilities in all three equities and also showed that volatility though present was not persistent in the three equities. For each of the companies under study, the GARCH (1, 1) model was found to outperform the other three models based on the comparison of the AICc for each model. The study recommended the use and comparison of other variants of the GARCH model in estimation of volatility.

Using EGARCH models to predict volatility in unconsolidated financial markets: the case of European carbon allowances

“…In this regard, the family of autoregressive conditional heteroskedasticity models (ARCH) (Bollerslev 1986 ; Engle 1982 ) has proven to be the best methods to measure volatility. Among the wide variety of extensions, the generalized ARCH (or GARCH) subtype stands out in recent literature on financial markets dynamics for developed regions such as the major European stock markets of the UK, France, and Germany (Olbrys & Majewska 2017 ); South Europe and Ireland (Bentes 2018 ); the USA (Aliyev et al 2020 ); and China (Do et al 2020 ), but also for less developed stock markets such as Pakistan (Mohsin et al 2020 ) or Ghana (Omari-Sasu et al 2015 ). GARCH models have also been applied to the analysis of exchange rate markets volatility (Hung 2021 ).…”