Portfolio Analysis Using Stochastic Dominance, Relative Entropy, and Empirical Likelihood

• 成果类型: Article
• 署名作者: Post, Thierry; Poti, Valerio
• 署名单位: Koc University; University College Dublin
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2015.2325
• 发表日期: 2017
• 页码: 153-165
• 关键词: Stochastic dominance; relative entropy; Empirical Likelihood; convex programming; UTILITY THEORY; portfolio theory; asset pricing

摘要

This study formulates portfolio analysis in terms of stochastic dominance, relative entropy, and empirical likelihood. We define a portfolio inefficiency measure based on the divergence between given probabilities and the nearest probabilities that rationalize a given portfolio for some admissible utility function. When applied to a sample of time-series observations in a blockwise fashion, the inefficiency measure becomes a likelihood ratio statistic for testing inequality moment conditions. The limiting distribution of the test statistic is bounded by a chi-squared distribution under general sampling schemes, allowing for conservative large-sample testing. We develop a tight numerical approximation for the test statistic based on a two-stage optimization procedure and piecewise linearization techniques. A Monte Carlo simulation study of the empirical likelihood ratio test shows superior small-sample properties compared with various generalized method of moments tests. An application analyzes the efficiency of a passive stock market index in data sets from the empirical asset pricing literature.