Title :
A Memory Reduction Monte Carlo Simulation for Pricing Multi-assets American Options
Author :
Yang, Haijun ; Wang, Cui
Author_Institution :
Sch. of Econ. & Manage., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
fDate :
March 31 2009-April 2 2009
Abstract :
When pricing American options on multi-assets (d) by Monte Carlo methods, one usually stores the simulated asset prices at all time steps on all paths in order to determine when to exercise the options. If N time steps and M paths are used, then the storage requirement is . It is undoubtedly enormous for Monte Carlo method which needs to increase the number of simulations to improve the accuracy. In this paper, we propose a memory reduction simulation method to price multi-asset American options and use it in low-discrepancy sequences. For machines with limited memory, we can now use larger values of M and N to improve the accuracy in pricing the options.
Keywords :
Monte Carlo methods; pricing; share prices; low-discrepancy sequence; memory reduction Monte Carlo simulation; multiassets American option; pricing; simulated asset price; Asset management; Calculus; Computational modeling; Computer science; Computer simulation; Costs; Engineering management; Memory management; Monte Carlo methods; Pricing; Low-discrepancy sequences; Memory reduction; Multi-asset American options;
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
DOI :
10.1109/CSIE.2009.192
