摘要：In this paper we use high-frequency multivariate data and attempt to model the joint distribution (dependence structure) of daily KSE-100 returns, S&P 500 and SSE 180 index. We compute portfolio Value at Risk (VaR) using Archimedean copula for three multivariate models, which were used to model the dependence structure of the three stock returns. We also compare the performances of these multivariate models based on the goodness of in-sample fit as well as backtesting of VaR results.
