Forecasting volatility in the South African stock market: A comparison of methods
Volatility prediction has become a crucial task in the appraisal of assets and risk management. Increased financial regulation with tougher capital standards and additional capital buffers has made understanding volatility in financial markets even more imperative. This study investigates and compares various forecasting techniques in their ability to forecast the South African Volatility Index (SAVI). In particular, a time-delay neural network’s forecasting ability is compared to those of more traditional methods. A comparison of the residual errors of all the forecasting tools used suggests that the time-delay neural network and the historical average model have superior forecasting ability over traditional forecasting models, with the naive historical average model having only slight superior forecasting ability than the neural network. From a practical perspective, this suggests that the historical average model is the best forecasting tool used in this study, as it is less computationally expensive to implement compared to the neural network. This result should however be interpreted with caution as only historical values of the SAVI were used as inputs to the neural network. In addition, the neural network may be better suited if the sample period were longer. Furthermore, the results suggest that the SAVI is extremely difficult to forecast, with the volatility index being purely a gauge of investor sentiment in the market, rather than being seen as a potential investment opportunity.