Using multivariate stochastic dominance to enhance portfolio selection and warn of financial crises

In this paper, we develop a portfolio optimization methodology that significantly improves upon conventional portfolio selection problems. In particular, we propose a two-step optimization problem where we first select the efficient assets based on alternative parametric assumptions and then maximize portfolio wealth using well-known performance measures. In the first optimization step, we employ multivariate stochastic dominance conditions to determine efficient assets for a class of non-satiable risk-averse investors. In the second optimization step, we maximize either reward-risk or drawdown-based performance measures on the selected assets. With this two-step optimization, we propose an early-warning system for stock market crises based on the information contained in the joint-losses of financial assets. We compare the performance of the proposed strategies with their conventional counterparts when the second optimization step is directly adopted on all assets. Empirical analyses of the US market validate the suggested approaches and highlight the implications of financial crises for portfolio selection problems. The results confirm that the proposed methodologies substantially improve upon the conventional approach for out-of-sample portfolios, from which managerial insights are drawn.