Digital Repository at the University of Maryland (DRUM) >
Robert H. Smith School of Business >
Decision, Operations & Information Technologies >
Decision, Operations & Information Technologies Research Works >
Please use this identifier to cite or link to this item:
|Title: ||Sensitivity Analysis for Monte Carlo Simulation of Option Pricing|
|Authors: ||Fu, Michael C.|
|Keywords: ||options pricing|
stochastic approximation algorithm
|Issue Date: ||1995|
|Citation: ||Probability in the Engineering and Informational Sciences, Vol. 9, No. 3, 417-446|
|Abstract: ||Monte Carlo simulation is one alternative for analyzing options markets
when the assumptions of simpler analytical models are violated.
We introduce techniques for the sensitivity analysis of option pricing
which can be efficiently carried out in the simulation.
In particular, using these techniques,
a single run of the simulation would often provide not only
an estimate of the option value
but also estimates of the sensitivities of the option value to
various parameters of the model.
Both European and American options are considered,
starting with simple
analytically tractable models to present the idea and
proceeding to more complicated examples.
We then propose an approach for
the pricing of options with early exercise features by
incorporating the gradient estimates in
an iterative stochastic approximation algorithm.
The procedure is illustrated in a simple example estimating
the option value of an American call.
Numerical results indicate that the additional computational
effort required over that required
to estimate a European option is relatively small.|
|Description: ||corrections to published article;
additional tables for numerical results|
|Appears in Collections:||Decision, Operations & Information Technologies Research Works|
All items in DRUM are protected by copyright, with all rights reserved.