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 Risk- Metrics one, resulting in higher performance of the G.E.D. method.