Dynamic copula-based expectile portfolios

This study investigates expectile Value-at-Risk (EVaR) as a risk measure in dynamic copula-based portfolio optimization, compared with the common variance and CVaR. To estimate the dependence structure between asset returns, the canonical vine copula augmented with the generalized additive models (GAMC-vine) is used. Applying multivariate conditional distributions from the GAMC-vine model, step-ahead asset return forecasts are obtained and used to construct dynamic copula-based EVaR portfolios. Using ten S&P 500 industry sectors, EVaR leads to a min-risk dynamic GAMC-vine portfolio that achieves higher out-of-sample average return and risk-adjusted ratios. Furthermore, EVaR shows a better portfolio ranking than CVaR. Moreover, the copula-based variance and EVaR portfolios show higher-order stochastic dominance compared to CVaR strategies. Finally, a subsample stochastic dominance analysis reveals that, in overall, the risk minimization does not benefit from the choice of risk modeling. However, the dynamic copula model leads to optimal portfolios that dominate the equally weighted benchmark more often compared to those from historical approach.