A nonparametric method for pricing and hedging American options

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
Pages691-700
StatePublished - 2013

Conference

Title43rd Winter Simulation Conference, WSC 2013 - Simulation: Making Decisions in a Complex World
PlaceUnited States
CityWashington, DC
Period8 - 11 December 2013

Abstract

In this paper, we study the problem of estimating the price of an American option and its price sensitivities via Monte Carlo simulation. Compared to estimating the option price which satisfies a backward recursion, estimating the price sensitivities is more challenging. With the readily-computable pathwise derivatives in a simulation run, we derive a backward recursion for the price sensitivities. We then propose nonparametric estimators, the k-nearest neighbor estimators, to estimate conditional expectations involved in the backward recursion, leading to estimates of the option price and its sensitivities in the same simulation run. Numerical experiments indicate that the proposed method works well and is promising for practical problems. © 2013 IEEE.

Citation Format(s)

A nonparametric method for pricing and hedging American options. / Feng, Guiyun; Liu, Guangwu; Sun, Lihua.

Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013. 2013. p. 691-700 6721462.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review