Modeling Dependence and Optimal Portfolios Using Vine Copula-GJR Approach: A China Stock Market Application

This paper constructs a framework using the vine copula-GJR model to capture the struct ure interdependence of assets and combines it with simulation studies to calculate the optimal portfolio, expected shortfall (ES), and component ES. This study aims to investigate the lever age effects of the top five firms in Shanghai Stock Exchange market, measure their interdepen dences, forecast their optimal portfolios, and identify the most risky firm at t + 1 period. The major empirical results show that the C-vines demonstrate a better performance than the D-vines, and the biggest contributor to the overall risk is PetroChina firm under optimal weights.