Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk

摘要

Value-at-Risk (VaR) is a widely used statistical measure in financial risk management for quantifying the level of risk associated with a specific investment portfolio. It is well-known that historical return data exhibit non-normal features, such as heavy tails and skewness. Current analytical (parameteric) calculation of VaR typically assumes the distribution of the portfolio return to be a normal or log-normal distribution, which results in underestimation and overestimation of the VaR at high and low confidence levels, respectively, when a normal distribution is assumed.