Two-Stage Stochastic International Portfolio Optimisation under Regular-Vine-Copula-Based Scenarios (original) (raw)

2017, arXiv (Cornell University)

An international portfolio allows simultaneous investment in both domestic and foreign markets. It hence has the potential for improved performance by exploiting a wider range of returns, and diversification benefits, than portfolios investing in just one market. However, to obtain the most efficient portfolios (along with the usual management of assets) the risks from currency fluctuations need good management, such as by using appropriate hedging. In this paper, we present a two-stage stochastic international portfolio optimisation model to find an optimal allocation for the combination of both assets and currency hedging positions. Our optimisation model allows a "currency overlay", or a deviation of currency exposure from asset exposure, to provide flexibility in hedging against, or in speculation using, currency exposure. The transaction costs associated with both trading and hedging are also included. To model the realistic dependence structure of the multivariate return distributions, a new scenario generation method, employing a regular-vine copula is developed. The use of vine copulas allows a better representation of the characteristics of returns, specifically, their non-normality and asymmetric dependencies. It hence improves the representation of the uncertainty underlying decisions needed for international portfolio optimisation problems. Efficient portfolios optimised with scenarios generated from the new vine-copula method are compared with the portfolios from a standard scenario generation method. Experimental results show that the proposed method, using realistic non-normal uncertainty, produces portfolios that give better risk-return reward than those from a standard scenario generation approach, using normal distributions. The difference in risk-return compensation is largest when the portfolios are constrained to require higher returns. The paper shows that it can be important to model the nonnormality in uncertainty, and not just assume normal distributions.