ROBUST MAXIMIZATION OF ASYMPTOTIC GROWTH UNDER COVARIANCE UNCERTAINTY

• 成果类型: Article
• 署名作者: Bayraktar, Erhan; Huang, Yu-Jui
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/12-AAP887
• 发表日期: 2013
• 页码: 1817-1840
• 关键词: eigenvalues; PRINCIPLE; maximum

摘要

This paper resolves a question proposed in Kardaras and Robertson [Ann. Appl. Probab. 22 (2012) 1576-1610]: how to invest in a robust growth-optimal way in a market where precise knowledge of the covariance structure of the underlying assets is unavailable. Among an appropriate class of admissible covariance structures, we characterize the optimal trading strategy in terms of a generalized version of the principal eigenvalue of a fully nonlinear elliptic operator and its associated eigenfunction, by slightly restricting the collection of nondominated probability measures.