GENERIC IMPROVEMENTS TO LEAST SQUARE MONTE CARLO METHODS FOR OPTIMAL STOPPING

发布日期:2019-07-08点击数:

报告人: 朱丹 (莫纳什大学)


日  期: 2019年710


时  间: 10:00


地  点: 理科楼 LD202


摘  要: This paper provides three generic improvements to least square Monte Carlo approaches for solving optimal stopping problems. First, we modify the regression specification to target the stopping boundary directly. Second, we employ Bayesian model averaging to select the set of basis functions used in the least square regression. Third, we formulate a single index regression to reduce the dimensionality without substantial compromise in results. Without loss of generality, we demonstrate the implementation of each improvement on a different early exercisable product and compare the results with established methods. We emphasise that the methods introduced are not only generic but are easily transplanted to any scenario where least square Monte Carlo is viable without any signi_cant increase in computational cost.


报告人简介朱丹,莫纳什大学高级讲师,主要研究金融数学,随机模拟等问题,在JoE,EJOR等杂志上发表过多篇论文。


公司联系人:张志民


欢迎广大师生积极参与!


关于我们
太阳成集团tyc539的前身是始建于1929年的太阳成集团理学院和1937年建立的太阳成集团商学院,理学院是太阳成集团最早设立的三个学院之一,首任经理为数学家何鲁先生。