Stochastic Bounds for Reference Sets in Portfolio Analysis
提出随机边界概念，即一个投资组合随机占优于参考集中的所有其他组合，并开发基于线性规划和子抽样的数值优化与统计推断方法，实证表明能提升投资绩效。
A stochastic bound is a portfolio that stochastically dominates all alternatives in a reference portfolio set instead of a single alternative portfolio. An approximate bound is a portfolio that comes as close as possible to this ideal. To identify and analyze exact or approximate bounds, feasible approaches to numerical optimization and statistical inference are developed based on linear programming and subsampling. The use of reference sets and stochastic bounds is shown to improve investment performance in representative applications to enhanced benchmarking using equity industry rotation and equity index options combinations. This paper was accepted by Kay Giesecke, finance.