emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short …-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

processes. The aim of this paper is to provide the characteristics of a causal multi-fractal model (replacing the earlier … 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. …

emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short …-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. …

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean … this paper together with a Monte Carlo study toinvestigate the small-sample properties of the SVM estimators. Takingthese …