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2009 WRI World Congress on Computer Science and Information Engineering
A Memory Reduction Monte Carlo Simulation for Pricing Multi-assets American Options
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
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.
Index Terms:
Memory reduction; Multi-asset American options; Low-discrepancy sequences
Citation:
Haijun Yang, Cui Wang, "A Memory Reduction Monte Carlo Simulation for Pricing Multi-assets American Options," csie, vol. 2, pp.312-316, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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