Recovering copulas from limited information and an application to asset allocation
提出一种基于熵的方法构建最大熵规范连接函数，用于投资者在道琼斯大盘股和小盘股指数间配置财富，理论分析和实证表明该方法能带来统计和经济上的巨大收益。
This paper proposes an entropy-based method to construct a new class of copulas – the most entropic canonical copulas (MECC). Our empirical study focuses on an investment problem for an investor with a constant relative risk aversion (CRRA) utility function allocating wealth between the Dow Jones Large-Cap and Small-Cap indices, of which the contemporaneous dependence can be modeled by the MECC or other commonly-used copulas. Both the theoretical analysis of the method and the empirical study indicate the potential for enormous statistical and economic gains as a result of using the MECC.