Memory-Reduction Method for Pricing American-Style Options under Exponential Levy Processes
Publication in refereed journal

香港中文大學研究人員

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摘要This paper concerns the Monte Carlo method in pricing American-style options under the general class of exponential Levy models. Traditionally, one must store all the intermediate asset prices so that they can be used for the backward pricing in the least squares algorithm. Therefore the storage requirement grows like O (mn), where m is the number of time steps and n is the number of simulated paths. In this paper, we propose a simulation method where the storage requirement is only O (m+n). The total computational cost is less than twice that of the traditional method. For machines with limited memory, one can now enlarge m and n to improve the accuracy in pricing the options. In numerical experiments, we illustrate the efficiency and accuracy of our method by pricing American options where the log-prices of the underlying assets follow typical Levy processes such as Brownian motion, lognormal jump-diffusion process, and variance gamma process.
著者Chan RH, Wu T
期刊名稱EAST ASIAN JOURNAL ON APPLIED MATHEMATICS
出版年份2011
月份2
日期1
卷號1
期次1
出版社GLOBAL SCIENCE PRESS
頁次20 - 34
國際標準期刊號2079-7362
電子國際標準期刊號2079-7370
語言英式英語
關鍵詞American options; exponential Levy processes; memory reduction; Monte Carlo simulation
Web of Science 學科類別Mathematics; Mathematics, Applied

上次更新時間 2020-16-10 於 23:55