Abstract
This study is aimed at investigating the volatility dynamics and the risk-return relationship in the South African market, analyzing the FTSE/JSE All Share Index returns for an updated sample period of 2009–2019. The study employed several GARCH type models with different probability distributions governing the model’s innovations. Results have revealed strong persistent levels of volatility and a positive risk-return relationship in the South African market. Given the elaborate use of the GARCH approach of risk estimation in the existing finance literature, this study highlighted several weaknesses of the model. A noteworthy property of the GARCH approach was that the innovation distributions did not affect parameter estimation. Analyzing the GARCH type models, this theory was supported by the majority of the GARCH test results with respect to the volatility dynamics. On the contrary, it was strongly unsupported by the risk-return relationship. More specifically, it was found that while the innovations of the EGARCH (1, 1) model could account for the volatile nature of financial data, asymmetry remained uncaptured. As a result, misestimating of risks occurred, which could lead to inaccurate results. This study highlighted the significance of the innovation distribution of choice and recommended the exploration of different nonnormal innovation distributions to aid with capturing the asymmetry.
Highlights
The condition of a country’s financial system is a key indicator of a country’s stability
It was found that the South African market was subject to strong persistent levels of volatility, which over a long period can be considered undesirable, as it can result in severe market instability
A fair amount of volatility can attract investors to seek a superior return, which exists in the South African market, since a positive risk-return relationship has been found
Summary
The condition of a country’s financial system is a key indicator of a country’s stability. For emerging markets, i.e., those of South Africa that show unique market return characteristics, are considered to have heavy tails and high levels of volatility, as pointed out by Adu et al (2015) To address these factors, there has been some extensions added to the standard GARCH model, as well as different nonnormal innovation distributions introduced to assist with capturing the asymmetry. Results revealed the DCC-MGARCH model to the daily data sets strong persistent volatility over time and signifiof the Korean market for the sample period from cant evidence of the asymmetric effects. Ilupeju (2016) applied the APARCH model for analysis of the daily data set of JSE ALSI returns, In a BRICS study, Adu et al (2015) used the ARMA- in addition to the GARCH models employed by EGARCH-M over the sample period from January Mandimika & Chinzara (2012), over the sample. Generalised Error Distribution (GED) in com- est stock exchange, the JSE was investigated where bination with the EGARCH-M, TARCH-M and the daily ALSI returns were analyzed
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