SOLVING OPTIMAL STOPPING PROBLEMS VIA EMPIRICAL DUAL OPTIMIZATION

• 成果类型: Article
• 署名作者: Belomestny, Denis
• 署名单位: University of Duisburg Essen
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/12-AAP892
• 发表日期: 2013
• 页码: 1988-2019
• 关键词: american; valuation; bounds

摘要

In this paper we consider a method of solving optimal stopping problems in discrete and continuous time based on their dual representation. A novel and generic simulation-based optimization algorithm not involving nested simulations is proposed and studied. The algorithm involves the optimization of a genuinely penalized dual objective functional over a class of adapted martingales. We prove the convergence of the proposed algorithm and demonstrate its efficiency for optimal stopping problems arising in option pricing.