Stochastic Bounds for Reference Sets in Portfolio Analysis

• 成果类型: Article
• 署名作者: Arvanitis, Stelios; Post, Thierry; Topaloglou, Nikolas
• 署名单位: Athens University of Economics & Business; Nazarbayev University; Athens University of Economics & Business; Athens University of Economics & Business
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3838
• 发表日期: 2021
• 页码: 7737-7754
• 关键词: portfolio analysis; stochastic dominance; linear programming; Subsampling; enhanced benchmarking

摘要

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.