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
Article

What We Can Learn from Pricing 139,879 Individual Stock Options

Lars Stentoft
The Journal of Derivatives Summer 2015, 22 (4) 54-78; DOI: https://doi.org/10.3905/jod.2015.22.4.054
Lars Stentoft
is an associate professor and Canada Research Chair in Financial Econometrics at the University of Western Ontario in London, ON, Canada.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: lars.stentoft@uwo.ca
  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
Loading

Click to login and read the full article.

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

Abstract

It has long been obvious that stock volatility is not a constant knowable parameter, as the original Black–Scholes model assumed, but no single extension to stochastic time-varying volatility has replaced it. The GARCH family of volatility models has the highly desirable feature that variance is a direct function of returns, so there is still only one source of risk, and volatility can be estimated easily from observed data. There are still unsettled issues in this framework, however, including which GARCH model to use, whether an asymmetry term should be included, and whether return shocks should be assumed to come from a normal distribution or some fatter-tailed alternative. In this article, Stentoft runs a horse race among GARCH-type models using the 30 stocks in the Dow Jones Industrial Average. An interesting innovation is to use the relatively new theory of model confidence sets in the testing procedure. The winner is NGARCH with normal inverse Gaussian errors.

  • © 2015 Pageant Media Ltd
View Full Text

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: 22 (4)
The Journal of Derivatives
Vol. 22, Issue 4
Summer 2015
  • Table of Contents
  • Index by author
Print
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.
What We Can Learn from Pricing 139,879 Individual Stock Options
(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
What We Can Learn from Pricing 139,879 Individual Stock Options
Lars Stentoft
The Journal of Derivatives May 2015, 22 (4) 54-78; DOI: 10.3905/jod.2015.22.4.054

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
What We Can Learn from Pricing 139,879 Individual Stock Options
Lars Stentoft
The Journal of Derivatives May 2015, 22 (4) 54-78; DOI: 10.3905/jod.2015.22.4.054
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
    • Abstract
    • THEORETICAL FRAMEWORK
    • RETURN DATA AND ESTIMATION RESULTS
    • OPTION DATA AND PRICING RESULTS
    • MODEL CONFIDENCE SETS FOR OPTION PRICING MODELS
    • CONCLUSION
    • Appendix
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Editor’s Letter
  • Editor’s Letter
  • Editor’s Letter
Show more 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