Canonical vine copulas in the context of modern portfolio management: Are they worth it?

•Clayton canonical vine copulas (CVC) successfully capture lower tail dependence.•CVC are mathematically scalable for portfolios of large dimensions (our study investigates its feasibility for portfolios of up to 12 constituents).•We focus on asset allocation for loss-averse investors by minimizing CVaR.•CVC can give improved investment performance.•The Clayton CVC can produce strongly positively skewed portfolios. In the context of managing downside correlations, we examine the use of multi-dimensional elliptical and asymmetric copula models to forecast returns for portfolios with 3–12 constituents. Our analysis assumes that investors have no short-sales constraints and a utility function characterized by the minimization of Conditional Value-at-Risk (CVaR). We examine the efficient frontiers produced by each model and focus on comparing two methods for incorporating scalable asymmetric dependence structures across asset returns using the Archimedean Clayton copula in an out-of-sample, long-run multi-period setting. For portfolios of higher dimensions, we find that modeling asymmetries within the marginals and the dependence structure with the Clayton canonical vine copula (CVC) consistently produces the highest-ranked outcomes across a range of statistical and economic metrics when compared to other models incorporating elliptical or symmetric dependence structures. Accordingly, we conclude that CVC copulas are ‘worth it’ when managing larger portfolios.