摘要:This paper examines VAR (Value at Risk) portfolio distribution using a version of GJR-GARCH based Student copulas. First, we extract filtered residuals for each return series through an asymmetric GJR-GARCH model. Then, we construct an empirical semi-parametric marginal cumulative distribution function (CDF) for each series using an estimation of a centred Gaussian root and an estimation of a generalised Pareto distribution (GPD) for the upper and lower tails. Our approach focuses on the entire distribution and on tail distribution as well. The GJR-GARCH co pula is then applied to the data and used to reduce correlation between the simulated residuals of each series. We present a methodology that links GJR-GARCH copula method with the CDF and a comparative study of the performance of algorithms estimating VAR to measure the impact of the GJR-GARCH on traditional VAR estimation results
关键词:GJR-GARCH; Copula model; Portfolio risk; and Value at Risk; and Conditional Value at Risk.