Time-varying conditional copula modelling of dependence structure in Indian financial markets: An empirical analysis

Abstract: This paper proposes skew-t copula with a time varying conditional correlation model to capture the time varying symmetric heavy-tail dependence structure among multi-asset classes, including government bonds, corporate bonds, equities, and real estate investment trusts (REITs). We provide new evidence that lower the dependence coefficients reflecting changes in the market volatility while maintaining symmetry between the upper and lower tails. The degree of joint heavy-tailed dependence among asset classes significantly increased and leading to a decline in the diversification benefit of multi-asset portfolios. Our empirical analysis shows that in terms of AIC and BIC, symmetric skew-t copula fits data of multi-asset classes better than to conventional time varying elliptical copulas and static t-copulas. Furthermore, out-of-sample analysis shows that considering a symmetry of heavy-tail behavior improves the expected shortfall (ES) estimation accuracy and enhances the performance of minimum-ES portfolios. The findings emphasize that capturing time-varying symmetric heavy-tail dependence is essential for accurate risk assessment and effective portfolio management.