Portfolio optimisation using constrained hierarchical bayes models

Jiangyong Yin and Xinyi Xu

Statistical Theory and Related Fields, 2017, vol. 1, issue 1, 112-120

Abstract: It is well known that traditional mean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors. Constrained solutions, such as no-short-sale-constrained and norm-constrained portfolios, can usually achieve much higher ex post Sharpe ratio. Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions. In this paper, we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model. We show that such constructed portfolios are well diversified with superior out-of-sample performance. Our proposed model is tested on a number of Fama–French industry portfolios against the naïve diversification strategy and Chevrier and McCulloch’s (2008) economically motivated prior (EMP) strategy. On average, our model outperforms Chevrier and McCulloch’s (2008) EMP strategy by over 15% and outperform the ‘1/N’ strategy by over 50%.

Date: 2017
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DOI: 10.1080/24754269.2017.1347310

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