A memory reduction method in pricing american options

Raymond H. CHAN, Yong CHEN, K. M. YEUNG

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)

Abstract

This paper is concerned with the pricing of American options by simulation methods. In the traditional methods, in order to determine when to exercise, we have to store the simulated asset prices at all time steps on all paths. If N time steps and M paths are used, then the storage requirement is O(MN). In this paper, we present a simulation method for pricing American options where the number of storage required only grows like O(M). The only additional computational cost is that we have to generate each random number twice instead of once. For machines with limited memory, we can now use a larger N to improve the accuracy in pricing the options.

Original languageEnglish
Pages (from-to)501-511
Number of pages11
JournalJournal of Statistical Computation and Simulation
Volume74
Issue number7
DOIs
Publication statusPublished - Jul 2004
Externally publishedYes

Keywords

  • Monte Carlo method
  • Option pricing
  • Random number generator

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