Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to …

in forecasting from using bivariate models remained small otherwise. …

. While the forecasting error of the combined forecast tends to be systematically smaller than that of the individual model …

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved …

forecasting financial volatility. We use the auto-covariances of log increments of the multi-fractal process in order to estimate … ?scaling? approach. Our empirical estimates are used in out-of-sample forecasting of volatility for a number of important …

Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … leads to gains in forecasting accuracy for some time series. …