SOLVING OPTIMAL STOPPING PROBLEMS VIA EMPIRICAL DUAL OPTIMIZATION

被引：19

作者：

Belomestny, Denis ^{[1
]}

机构：

[1] Duisburg Essen Univ, D-45127 Essen, Germany

来源：

ANNALS OF APPLIED PROBABILITY | 2013年 / 23卷 / 05期

关键词：

Optimal stopping; simulation-based algorithms; functional optimization; empirical variance; self-normalized processes;

D O I：

10.1214/12-AAP892

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：1988 / 2019

页数：32

共 16 条

[1] Primal-dual simulation algorithm for pricing multidimensional American options [J].