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A Memory Reduction Monte Carlo Simulation for Pricing Multi-assets American Options

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2 Author(s)
Haijun Yang ; Sch. of Econ. & Manage., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China ; Cui Wang

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.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:2 )

Date of Conference:

March 31 2009-April 2 2009