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

A Bayesian View on Autocallable Pricing and Risk Management

Tommaso Paletta and Radu Tunaru
The Journal of Derivatives Summer 2022, jod.2022.1.161; DOI: https://doi.org/10.3905/jod.2022.1.161
Tommaso Paletta
works in the model validation team of J.P. Morgan & Chase covering interest rates products and hybrids. Prior to this, he worked in ING Bank in the Netherlands as model reviewer in the counterparty risk space, also holds a Ph.D. in Computational Finance from the University of Kent, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Radu Tunaru
is a professor in finance and risk management at the University of Sussex Business School in Falmer, Brighton, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • 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

In this article, some insights are presented on the risks associated with trading autocallable financial products. This class of structured products survived the Lehman Brothers collapse in 2008 and the sovereign crisis of 2011 but was deeply affected by the emergence of the COVID-19 pandemic in 2020. This article highlights the important role played by dividend risk, which was neglected until 2020 in the derivatives literature on equity structured products. The article also emphasizes that both equity volatility uncertainty and dividend uncertainty play a crucial role in pricing and risk-managing autocallables. The article uses the Black-Scholes model in a Bayesian setup, demonstrating how volatility uncertainty affects the estimation of dividend yield and vice versa.

  • © 2022 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: 30 (3)
The Journal of Derivatives
Vol. 30, Issue 3
Spring 2023
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
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.
A Bayesian View on Autocallable Pricing and Risk Management
(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
A Bayesian View on Autocallable Pricing and Risk Management
Tommaso Paletta, Radu Tunaru
The Journal of Derivatives May 2022, jod.2022.1.161; DOI: 10.3905/jod.2022.1.161

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
A Bayesian View on Autocallable Pricing and Risk Management
Tommaso Paletta, Radu Tunaru
The Journal of Derivatives May 2022, jod.2022.1.161; DOI: 10.3905/jod.2022.1.161
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
    • Abstract
    • UNDERSTANDING THE RISKS ASSOCIATED WITH AUTOCALLABLES
    • SOME ILLUSTRATIVE EXAMPLES
    • MARCH 2020 CRASH: INVESTIGATION ON THE MODEL RISK
    • MODEL RISK IN AUTOCALLABLES
    • PRICING EXAMPLES
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • No citing articles found.
  • Google Scholar
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
reply@pm-research.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

© 2023 With Intelligence Ltd | All Rights Reserved | ISSN: 1074-1240 | E-ISSN: 2168-8524

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