Portfolio management using time-varying vine copula: an application on the G7 equity market indices

Abstract

We consider structural breaks and use vine copulas to hierarchically model the underlying assets’ dependence structure of the portfolio of G7 equity market indices (1998–2019). This framework is noticed for its flexibility in capturing asymmetry and non-linearity in a time-varying style. We compare the portfolio performance in terms of the minimum Conditional Value-at-risk (CVaR) and the maximum return-to-CVaR ratio criteria with the traditional mean-variance framework and the equal-weighted strategy. The outcomes show the outperformance of our method across subperiods. Canonical vine copula marginally outperforms drawable vine copula in terms of return-to-risk ratio. Our proposed vine copula models better capture the risk-return tradeoff especially during critical market moments.