explores various specifications of decompositions and various forecasting experiments. The result from these horse-races is … for richer forecasting specifications, the paper shows, using Bayesian model averaging techniques (BMA), that the …

We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and …

forecasting occur much less frequently than indicated by existing tests. …

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 …

(asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We … variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared … topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk …

-of-sample, for daily returns on the Standard & Poor's100 index. Similar studies have been undertaken with GARCH models where … returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …

We develop a new dynamic multivariate model for the analysis and the forecasting of football match results in national … model specification yield the best forecasting results. In an extensive forecasting study, we consider match results from …

surface types. We finally show that our proposed model can also be effective in forecasting. We provide evidence that our … model significantly outperforms existing models in the forecasting of tennis match results. …