A Generalized Error Distribution-Based Method for Conditional Value-at-Risk Evaluation

One of the most important issues in finance is to correctly measure the risk profile of a portfolio, which is fundamental to take optimal decisions on the capital allocation. In this paper, we deal with the evaluation of portfolio’s Conditional Value-at-Risk (CVaR) using a modified Gaussian Copula, where the correlation coefficient is replaced by a generalization of it, obtained as the correlation parameter of a bivariate Generalized Error Distribution (G.E.D.). We present an algorithm with the aim of verifying the performance of the G.E.D. method over the classical RiskMetrics one, resulting in higher performance of the G.E.D. method.