This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. With our new methodology, the original multivariate, n-dimensional model is treated as a set...

It is well documented in the literature that the forecasts of Value-at-Risk (VaR) and Expected Shortfall (ES) can be improved by additional high-frequency information (i.e., realized volatility). However, existing framework provides no apparent way of integrating effective low-frequency signals....

This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...

We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because it meets by construction ex post the correct out-of-sample conditional coverage criterion. This enables a Wald-type conditional quantile forecast encompassing...