Volatility Forecast Combinations using Asymmetric Loss Functions

The paper deals with the problem of model uncertainty in forecasting volatility using forecast combinations and a flexible family of asymmetric loss functions that allow for the possibility that an investor would attach different preferences to high vis-a-vis low volatility periods. Using daily as well as 5 minute data for US and major international stock market indices we provide volatility forecasts by minimizing the Homogeneous Robust Loss function of the Realized Volatility and the combined forecast. Our findings show that forecast combinations based on the homogeneous robust loss function significantly outperform simple forecast combination methods, especially during the period of the recent financial crisis.