Knowledge Bank

Although forecasting volatility is an important component of assessing financial risks, it is difficult for many investors because most methods require advanced mathematical knowledge. However, there are two types of time-series models, generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) and exponentially weighted moving average (EWMA), that can be used by investors with only basic training. Furthermore, the implied volatility indexes launched by the Chicago Board Options Exchange (CBOE) provide investors with a direct assessment of market volatility. However, it is unclear which of these three models is best for individual investors. To find out which of these three is the best forecasting method for investors to use, this research first checks whether implied volatility indexes can provide more accurate forecasts than GARCH (1,1) and EWMA by comparing the predictive ability of 11 implied volatility indexes (namely, VIX, VXST, VIX3M, VXMT, VXO, VXD, RVX, VXN, VFTSE, VHSI, and VHSI) with that of GARCH (1,1) and EWMA for the underlying stock indexes. Second, this research focuses on comparing in detail the volatility forecasting ability of GARCH (1,1) and that of EWMA to find which is the best method when volatility indexes are not available or volatility indexes are not good to use. The root mean-square error (RMSE) is used to examine the predictive ability of the three volatility forecasting methods mentioned and the results show that the implied volatility indexes perform better than the GARCH (1,1) and EWMA models for stock indexes in most situations. Additionally, it is shown that GARCH (1,1) has stronger forecasting powers than EWMA for stock indexes. Overall, most implied volatility indexes can be regarded as good forecasts of future volatility to be used by investors in the markets. If an implied volatility index is unavailable or not suitable for a particular case, averaging the forecasts from GARCH (1,1) and EWMA would be a good way to ensure investors get relatively accurate forecasts.