We present a framework focused on the interdependence of high-dimensional tail events. This framework allows us to analyze and quantify tail interdependence at different levels of extremity, decompose it into systemic and residual part and to measure the contribution of a constituent to the...

We define the Multidimensional Value at Risk (MVaR) as a natural generalization of VaR. This generalization makes a number of important applications possible. For example, many techniques developed for VaR can be applied to MVaR directly. As an illustration, we employ VaR forecasting and...

We present a framework focused on the interdependence of high-dimensional tail events. This framework allows us to analyse and quantify tail interdependence at different levels of extremity, decompose it into systemic and residual part and to measure the contribution of a constituent to the...

Value-at-Risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a new simple approach to estimation of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting...