This paper applies LINEX loss functions to forecasting nonlinear functions of variance. We derive the optimal one …. Our results suggest that the LINEX loss function is particularly well-suited to many of these forecasting problems and can …

This paper applies LINEX loss functions to forecasting nonlinear functions of variance. We derive the optimal one …. Our results suggest that the LINEX loss function is particularly well-suited to many of these forecasting problems and can …

large number of forecasting models have been designed to forecast crude oil prices' volatility, so far the relative … performance evaluation of competing forecasting models remains an exercise that is unidimensional in nature. To be more specific …

, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances … over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …