The paper analyses the forecasting performance of a variety of statistical and econometric models of UK FTA All Share and FTSE100 stock index volatility at the monthly, weekly and daily frequencies under both symmetric and asymmetric loss functions. Under symmetric loss, results suggest that the random walk model provides vastly superior monthly volatility forecasts, while random walk, moving average, and recursive smoothing models provide moderately superior weekly volatility forecasts, and GARCH, moving average and exponential smoothing models provide marginally superior daily volatility forecasts. If attention is restricted to one forecasting method for all frequencies, the most consistent forecasting performance is provided by moving average and GARCH models. More generally, results suggest that previous results reporting that the class of GARCH models provide relatively poor volatility forecasts may not be robust at higher frequencies, failing to hold here for the crash-adjusted FTSE100 index in particular.

We forecast realized volatility extending the heterogeneous autoregressive model (HAR) to include implied volatility (IV), the leverage effect, overnight returns, and the volatility of realized volatility. We analyze 10 international stock indices finding that, although a simple HAR model augmented with IV (HAR‐IV) is more accurate than any HAR model excluding it, all markets support further extensions of the HAR‐IV model. More accurate forecasts are found using overnight returns in all markets except the UK, the volatility of realized volatility in the US, and the leverage effect in five markets. A value‐at‐risk exercise supports the economic significance of our findings.