Name
Abstract | ||
|---|---|---|
Monte Carlo simulation techniques that use function approximations have been successfully applied to approximately price multi-dimensional American options. However, for many pricing problems the time required to get accurate estimates can still be prohibitive, and this motivates the development of variance reduction techniques. In this paper, we describe a zero-variance importance sampling measure for American options. We then discuss how function approximation may be used to approximately learn this measure; we test this idea in simple examples. We also note that the zero-variance measure is fundamentally connected to a duality result for American options. While our methodology is geared towards developing an estimate of an accurate lower bound for the option price, we observe that importance sampling also reduces variance in estimating the upper bound that follows from the duality. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/WSC.2004.1371367 | Simulation Conference, 2004. Proceedings of the 2004 Winter |
Keywords | Field | DocType |
Markov processes,covariance analysis,function approximation,importance sampling,pricing,probability,regression analysis,simulation,Monte Carlo simulation technique,function-approximation-based importance sampling,multidimensional American option pricing problem,zero-variance measure | Econometrics,Binomial options pricing model,Monte Carlo methods for option pricing,Importance sampling,Mathematical optimization,Monte Carlo method,Function approximation,Computer science,Upper and lower bounds,Duality (optimization),Variance reduction | Conference |
Volume | ISBN | Citations |
1 | 0-7803-8786-4 | 8 |
PageRank | References | Authors |
0.89 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nomesh Bolia | 1 | 29 | 3.83 |
| Sandeep Juneja | 2 | 404 | 59.50 |
| Paul Glasserman | 3 | 496 | 95.86 |