Forecasting the volatility of crude oil futures: A time-dependent weighted least squares with regularization constraint

Parameter instability and model uncertainty are two key problems affecting forecasting outcomes. In this paper, we propose a time-dependent weighted least squares with ridge constraint (TWLS-Ridge) to solve the above two problems in the forecasting procedure. The new TWLS-Ridge approach is applied to the heterogenous autoregressive realized volatility model and its various extensions. The empirical results suggest that TWLS-Ridge produces more accurate volatility forecasts than several alternative models dealing with parameter instability and model uncertainty. The superior performance of TWLS-Ridge is robust under different forecast horizons, evaluation periods, and loss functions. An investor with mean-variance preference can improve utility using TWLS-Ridge forecasts of oil volatility instead of ordinary least squares model forecasts.