Skip to main content

Main menu

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JOD
    • Editorial Board
    • Published Ahead of Print (PAP)
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

User menu

  • Sample our Content
  • Request a Demo
  • Log in

Search

  • ADVANCED SEARCH: Discover more content by journal, author or time frame
The Journal of Derivatives
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Sample our Content
  • Request a Demo
  • Log in
The Journal of Derivatives

The Journal of Derivatives

ADVANCED SEARCH: Discover more content by journal, author or time frame

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JOD
    • Editorial Board
    • Published Ahead of Print (PAP)
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter
Primary Article

Variance Reduction for Multivariate Monte Carlo Simulation

Jr-Yan Wang
The Journal of Derivatives Fall 2008, 16 (1) 7-28; DOI: https://doi.org/10.3905/jod.2008.710895
Jr-Yan Wang
An assistant professor with the Department of International Business at National Taiwan University in Taipei, Taiwan.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: jywang@management.ntu.edu.tw
  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
Loading

Abstract

As derivative securities become more complex, solution by Monte Carlo simulation is an increasingly necessary tool. But Monte Carlo methods are computationally demanding, and the size of the simulation sample required to achieve reasonable accuracy rapidly escalates beyond what is feasible given current technology when multiple stochastic factors are involved. Variance reduction techniques help considerably. One of the simplest is use of antithetic variables, which imposes on the simulated data the true constraint that the distribution from which the sample is to be drawn is symmetric. Moment matching goes further, by constraining the mean and variance of the sample to match the desired values. In this article, Wang goes further still, to show how to force simulated multivariate vectors to have the right correlations. In the first step a multivariate sample of independent variables is simulated and its sample correlation matrix is calculated. This will be close to the identity matrix but not exactly equal, due to sampling noise. Cholesky factorization of the sample correlation matrix is then used to transform the initial sample of slightly correlated factors into one whose elements are perfectly uncorrelated in the sample. A second Cholesky factorization of the target correlation matrix that is desired for the variables transforms them into a sample with exactly the correct correlations. As Wang demonstrates, this produces a sharp increase in performance for multivariate Monte Carlo problems, even when the simulation sample is constructed from nonrandom low-discrepancy sequences rather than by stochastic simulation.

  • © 2008 Pageant Media Ltd

Don’t have access? Click here to request a demo

Alternatively, Call a member of the team to discuss membership options

US and Overseas: +1 646-931-9045

UK: 0207 139 1600

Log in using your username and password

Forgot your user name or password?
PreviousNext
Back to top

Explore our content to discover more relevant research

  • By topic
  • Across journals
  • From the experts
  • Monthly highlights
  • Special collections

In this issue

The Journal of Derivatives
Vol. 16, Issue 1
Fall 2008
  • Table of Contents
  • Index by author
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on The Journal of Derivatives.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Variance Reduction for Multivariate Monte Carlo Simulation
(Your Name) has sent you a message from The Journal of Derivatives
(Your Name) thought you would like to see the The Journal of Derivatives web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Variance Reduction for Multivariate Monte Carlo Simulation
Jr-Yan Wang
The Journal of Derivatives Aug 2008, 16 (1) 7-28; DOI: 10.3905/jod.2008.710895

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Save To My Folders
Share
Variance Reduction for Multivariate Monte Carlo Simulation
Jr-Yan Wang
The Journal of Derivatives Aug 2008, 16 (1) 7-28; DOI: 10.3905/jod.2008.710895
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • Demystifying Credit Risk Derivatives and Securitization: Introducing the Basic Ideas to Undergraduates
  • Google Scholar

More in this TOC Section

  • The Subprime Credit Crisis of 2007
  • The Determinants of CDS Bid-Ask Spreads
Show more Primary Article
LONDON
One London Wall, London, EC2Y 5EA
United Kingdom
+44 207 139 1600
 
NEW YORK
41 Madison Avenue, New York, NY 10010
USA
+1 646 931 9045
pm-research@pageantmedia.com
 

Stay Connected

  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

MORE FROM PMR

  • Home
  • Awards
  • Investment Guides
  • Videos
  • About PMR

INFORMATION FOR

  • Academics
  • Agents
  • Authors
  • Content Usage Terms

GET INVOLVED

  • Advertise
  • Publish
  • Article Licensing
  • Contact Us
  • Subscribe Now
  • Log In
  • Update your profile
  • Give us your feedback

© 2021 Pageant Media Ltd | All Rights Reserved | ISSN: 1074-1240 | E-ISSN: 2168-8524

  • Site Map
  • Terms & Conditions
  • Privacy Policy
  • Cookies