Risk–return Trade‐off and Behaviour of Volatility on the South African Stock Market: Evidence From Both Aggregate and Disaggregate Data

Abstract

The study analyses the nature and behaviour of volatility, the risk–return relationship and the long‐term trend of volatility on the South African equity markets using aggregate level, industrial level and sectoral level daily data for the period 1995‐2009. By employing dummy variables for the Asian and the sub‐prime financial crises and the 11 September political shock, the study further examines whether the long‐term trend of volatility structurally breaks during financial crises and major political shocks. Three time‐varying generalised autoregressive conditional heteroskedasticity models were employed: one of them symmetric, and the other two asymmetric. Each of these models was estimated based on three error distributional assumptions. The findings of the study are as follows: First, volatility is largely persistent and asymmetric. Second, risk at both aggregate and disaggregate level is generally not a priced factor on the South Africa (SA) stock market. Third, the threshold autoregressive conditional heteroscedasticity (TARCH) model under the generalised error distribution is the most appropriate model for conditional volatility of the SA stock market. Fourth, volatility generally increases over time, and its trend structurally breaks during financial crises and major global shocks. The policy and investment implications of the findings are outlined.

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

“…Thirdly, thin trading along with non-trading days (i.e. holidays and weekends) generates serious concerns regarding the use of daily data (Mandimika and Chinzara, 2012). Furthermore, the use of monthly data is in accordance with strong financial literature (e.g.…”

“…see Elyasiani and Mansur, 1998;Elyasiani et al, 2011;Mandimika & Chinzara, 2012). For instance, the statistical significance of Jarque-Bera statistics coupled with the values of skewness and kurtosis reveals that the distribution of data series departs from normality.…”

“…Furthermore, it is yet possible that the asymmetric volatility might rest on the three other theories, namely time varying risk premia theory, asymmetric volatility of economic factors theory and/or both leverage (financial) and volatility feedback effect theory simultaneously (e.g. see Duffee, 1995;French & Sichel, 1993;French et al, 1987;Karmakar, 2007;Mandimika & Chinzara, 2012;Pindyck, 1984 andSchwert, 1989) All these theories are discussed in detail in Section 3.2.…”

“…However, the emphasis on the association between risk and returns has been put under significant stress recently by the financial press. This designates the significance of risk while pricing the financial assets (Mandimika & Chinzara, 2012). More so, Merton (1973) signified that at aggregate market level, the required excess return is represented by a positive function of their conditional variance.…”

“…see Balios, 2008;Campbell & Hentschel, 1991;Dimitriou & Simos, 2011;Fraser & Power, 1997;French, Schwert, & Stambaugh, 1987;Glosten, Jagannathan, & Runkle, 1993;Hansson & Hordahl, 1998;Jiranyakul, 2011;Koutmos, Negakis, & Theodossiou, 1993;Lanne & Saikkonen, 2004;Lebaron, 1989;Lettau & Ludvigson, 2010;Li, Yang, & Hsiao, 2005;and Mandimika & Chinzara, 2012), ascribed mixed evidence regarding the existence of risk premium in the stock markets of Australia, the USA, Europe, Asia and Africa by applying the GARCH-M model. For instance, Li et al (2005) concluded negative risk premium for six out of 12 markets, and so does by Mandimika and Chinzara (2012) while examining the South African stock market. However, on the contrary, the study of French et al (1987) and Yu and Hassan (2008) in the Middle East and North African region and Jiranyakul (2011) in Thailand accredited a positive risk-return trade-off declaring a positive risk premium.…”