Robust multivairiate extreme value at risk allocation

Abstract

Recent research in economy, especially in finance, is using the assumption that the returns distribution is normal for determining optimal allocation for different models in finance optimization like Markowitz model. However this assumption is not already exact because the problem of the asymmetric distribution has a big impact. Our paper proposes a Multivariate Extreme Value at Risk (MEVaR) approach to find the optimal allocation of a portfolio based on the extreme value theory. A detailed procedure and implementation on two different portfolio (the first, for an emerging market as Morocco and the second on a Canadian portfolio of a very liquid market. Are given for demonstrating the consistency of the new approach? Results are compared with the Worst-Case-Value at Risk (WCVaR) proposed by El Ghaoui (2003), and Partitioned Value at Risk (PVaR) approach Joel Goh et al. (2011). They establish that the MEVaR performs well and is used in prediction model.