Forecasting International Stock Market Variances

We examine 320 different forecasting models for international monthly stock return volatilities, using high frequency realized variances and the implied option variance as the predictor variables. We evaluate linear and non-linear models, and logarithmic transformed and weighted least squares estimation approaches. A logarithmically transformed Corsi (2009) model combined with the option implied variance (“lm4 log†) is robustly, across countries and time, among the best forecasting models. It also survives tests using panel models and international variables. When alternative models (such as models including negative returns) have better performance, the forecasts they generate are extremely highly correlated with those of the “lm4 log†model.