Pricing multi-asset American-style options by memory reduction Monte Carlo methods

Raymond H. CHAN, Chi Yan WONG*, Kit Ming YEUNG

*Corresponding author for this work

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

16 Citations (Scopus)

Abstract

When pricing American-style options on d assets 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 d · M · N. In this paper, we give a simulation method to price multi-asset American-style options, where the storage requirement only grows like (d + 1)M + N. 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 larger values of M and N to improve the accuracy in pricing the options.

Original languageEnglish
Pages (from-to)535-544
Number of pages10
JournalApplied Mathematics and Computation
Volume179
Issue number2
DOIs
Publication statusPublished - 15 Aug 2006
Externally publishedYes

Keywords

  • American-style options
  • Memory reduction method
  • Monte Carlo method
  • Multi-asset
  • Random number

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