Forecasting changes in the South African volatility index: A comparison of methods

Increased financial regulation with tougher capital standards and additional capital buffers has made understanding volatility in financial markets more imperative. This study investigates 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 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. 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. 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.