We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The … asset returns. Our model outperforms the benchmarks in fore-casting the inflation level, its conditional variance and the … volatility of returns. Moreover, we are able to predict all the conditional covariances among the observable series. …

of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting … errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are … of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility …

volatility smiles or interest rate curves. Recently, Pelsser and Lord [11] raised the question whether PCA results may not be …

Das in Finanzmarktdaten zu beobachtende volatility-clustering impliziert, daß große Renditeschocks bei der Preisbildung …The volatility clustering observed in financial market data implies that large net yield shocks increase the … probability of a higher future volatility during the price formation. Starting from the ARCH models which were suggested by Engle …

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean …(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable … Stochastic Volatility (SV)model. However, efficient Monte Carlo simulationmethods for SV models have been developed to overcome …

returns with prediction models from the ARCH, stochastic volatility and Markov mixture families. In this example models that …

applicability of a multivariate constant conditional correlation version of the model to national stock market returns for eight … transformation are remarkably similar across countries. Out-of-sample evidence for the superior forecasting ability of the …