  Volatility forecasts are key inputs in financial analysis. While lasso based
forecasts have shown to perform well in many applications, their use to obtain
volatility forecasts has not yet received much attention in the literature.
Lasso estimators produce parsimonious forecast models. Our forecast combination
approach hedges against the risk of selecting a wrong degree of model
parsimony. Apart from the standard lasso, we consider several lasso extensions
that account for the dynamic nature of the forecast model. We apply forecast
combined lasso estimators in a comprehensive forecasting exercise using
realized variance time series of ten major international stock market indices.
We find the lasso extended "ordered lasso" to give the most accurate realized
variance forecasts. Multivariate forecast models, accounting for volatility
spillovers between different stock markets, outperform univariate forecast
models for longer forecast horizons.
