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 min imization 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 (AD) 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 modelling 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 com pared to other models incorporating elliptical or symmetric dependence structures. Accordingly, we conclude that CVC copulas are 'worth it' when managing larger portfolios.
|Title of host publication||Assymetric Dependence in Finance|
|Subtitle of host publication||Diversification, Correlation and Portfolio Management in Market Downturns|
|Editors||Jamie Alcock, Stephen Satchell|
|Place of Publication||Chichester|
|Publisher||John Wiley & Sons|
|Number of pages||27|
|Publication status||Published - 27 Mar 2017|