@Article{EAJAM-1-1,
author = {},
title = {Memory-Reduction Method for Pricing American-Style Options under Exponential Lévy Processes},
journal = {East Asian Journal on Applied Mathematics},
year = {2011},
volume = {1},
number = {1},
pages = {20--34},
abstract = {<p style="text-align: justify;">This paper concerns the Monte Carlo method in pricing American-style options
under the general class of exponential Lévy 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 Lévy processes such as Brownian motion, lognormal jump-diffusion process,
and variance gamma process.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.020310.120410a},
url = {https://global-sci.com/article/82857/memory-reduction-method-for-pricing-american-style-options-under-exponential-levy-processes}
}