Adaptive combinations of tail-risk forecasts

Abstract

In order to meet the increasingly stringent global standards of banking management and regulation, several methods have been proposed in the literature for forecasting tail risk measures such as the Value-at-Risk (VaR) and Expected Shortfall (ES). However, regardless of the approach used, there are several sources of uncertainty, including model specifications, data-related issues and the estimation procedure, which can significantly affect the accuracy of VaR and ES measures. Aiming to mitigate the influence of these sources of uncertainty and improve the predictive performance of individual models, we propose novel forecast combination strategies based on the Model Confidence Set (MCS). In particular, consistent joint VaR and ES loss functions within the MCS framework are used to adaptively combine forecasts generated by a wide range of parametric, semi-parametric, and non-parametric models. Our results reveal that the proposed combined predictors provide a suitable alternative for forecasting risk measures, passing the usual backtests, entering the set of superior models of the MCS, and usually exhibiting lower standard deviations than other model specifications.