Modeling return volatility on the JSE sectors
Modelling and forecasting volatility are essential functions in different fields of finance, particularly in the quantitative risk management departments of banks and insurance companies. Volatility within the stock market can be forecasted. However, the debate is centred around how far ahead one can accurately forecast and to what extent changes to volatility can be made. Volatility has an impact on investment decisions, risk management, monetary policy decisions and security valuation. This study aims to unpack the impact of volatility on investment decisions. Investment is very low in the South African economy because South Africa is perceived as an economy of spenders with little savings and investments, which results in low economic growth rates and a stagnant economy. Volatility exists in various economic sectors, which makes it difficult for investors to make decisions as to which sector to invest in. As a result, it is important to be able to forecast volatility on investment decisions, so that investors can make decisions that are more informed. The study primarily focused on modelling the most volatile sector in the top five JSE sectors according to market capitalisation. The primary objective was achieved with the use of volatility models, namely the autoregressive conditional heteroscedastic (ARCH); generalised autoregressive conditional heteroscedastic (GARCH); threshold autoregressive conditional heteroscedastic/ Glosten-Jagannathan-Runkle (TGARCH/GJR); and exponential generalised autoregressive conditional heteroscedastic (EGARCH) models to determine the most volatile JSE sector. The study used a quantitative approach with secondary data ranging over a period of 13 years starting from January 2002 to December 2015. The sample used in the study consists of daily data obtained from McGregor INET/ BFA, the JSE and the South African Reserve Bank (SARB). The study examined the most volatile JSE sector amongst the top five JSE sectors according to market capitalisation. This was achieved by using the abovementioned ARCH/ GARCH volatility models. The results of this study revealed that according to the descriptive statistics, the JSE consumer goods sector is the most volatile sector due to its standard deviation value and the deviation of this sector’s returns to its mean value, the standard deviation is the most accurate measure of volatility. Furthermore, for model selection, the EGARCH and TGARCH models were classified as the best volatility capturing models. This was determined by the model criteria of having the lowest Akaike information criterion (AIC) and Schwarz information criterion (SC) values. The EGARCH model was best suited for consumer goods and financial sectors and TGARCH model was best suited for industrial, basic materials and consumer services’ sectors. The information gained from the volatility models will guide investors with valuable information about which sector to invest in and how best to diversify their investment portfolios according to their risk appetites as well as guiding investors with information, such as which sector is the most volatile to generate higher returns, as well as understanding the correlational relationship between the sectors and if there is a spill-over effect between the sectors.