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

Summary/Abstract: Increased financial regulation with tougher capital standards and additional capital buffers hasmade understanding volatility in financial markets more imperative. This study investigates variousforecasting techniques in their ability to forecast the South African Volatility Index (SAVI). In particular, atime-delay neural network’s forecasting ability is compared to more traditional methods. A comparison of theresidual errors of all the forecasting tools used suggests that the time-delay neural network and the historicalaverage model have superior forecasting ability over traditional forecasting models. From a practicalperspective, this suggests that the historical average model is the best forecasting tool used in this study, as itis less computationally expensive to implement compared to the neural network. Furthermore, the resultssuggest that the SAVI is extremely difficult to forecast, with the volatility index being purely a gauge ofinvestor sentiment in the market, rather than being seen as a potential investment opportunity.

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