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 …

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

as semiparametric time series models are evaluated in terms of fitting and ex ante forecasting. The overall impact of …

returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …