학술논문

On application of nonparametric regression estimation to options pricing
Document Type
Conference
Source
2009 IEEE International Symposium on Information Theory Information Theory, 2009. ISIT 2009. IEEE International Symposium on. :1579-1583 Jun, 2009
Subject
Computing and Processing
Pricing
Recursive estimation
Upper bound
Monte Carlo methods
Linear regression
State estimation
Application software
Computer science
Economic indicators
Language
ISSN
2157-8095
2157-8117
Abstract
We consider American options also called Bermudan options in discrete time.We use the dual approach to derive upper bounds on the price of such options using only a reduced number of nested Monte Carlo steps. The key idea is to use nonparametric regression to estimate continuation values and all other required conditional expectations and to combine the resulting estimate with another estimate computed by using only a reduced number of nested Monte Carlo steps. The mean value of the resulting estimate is an upper bound on the option price. One can show that the estimates of the option prices are universally consistent, i.e., they converge to the true price regardless of the structure of the continuation values. The finite sample behavior is validated by experiments on simulated data.