shown to be specific for commodity and market. A forecasting comparison on the basis of the identified models suggests that …

in forecasting from using bivariate models remained small otherwise. …

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

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. …

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