Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.192
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.
Memory reduction; Multi-asset American options; Low-discrepancy sequences
Haijun Yang, Cui Wang, "A Memory Reduction Monte Carlo Simulation for Pricing Multi-assets American Options", Computer Science and Information Engineering, World Congress on, vol. 02, no. , pp. 312-316, 2009, doi:10.1109/CSIE.2009.192