Skip Navigation


IMA Journal of Management Mathematics Advance Access originally published online on February 1, 2006
IMA Journal of Management Mathematics 2006 17(3):289-303; doi:10.1093/imaman/dpi044
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
17/3/289    most recent
dpi044v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Shao, Q.-M.
Right arrow Articles by Yu, H.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The authors 2006. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

A calibrated scenario generation model for heavy-tailed risk factors

Qi-Man Shao1, Hao Wang**,2 and Hao Yu3

1 Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong and University of Oregon, Eugene, OR 97403, USA, 2 Department of Mathematics, University of Oregon, Eugene, OR 97403, USA, 3 Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario N6A 5B7, Canada

** Email: haowang{at}uoregon.edu

In this paper, a calibrated scenario generation model for multivariate risk factors with heavy-tailed distributions is developed. This model includes the standard and classical model of scenario generation developed by J. P. Morgan as a special case. A rotation method is introduced to preserve the correlation information between risk factors, and a mixture of normal distributions is used to model and fit each marginal heavy-tailed distribution. Based on the scenario generation, a non-parametric method is applied to estimate the extreme value-at-risk and value-at-risk confidence interval of a portfolio with heavy-tailed distribution.

Keywords: VaR; VaR CI; heavy tails; non-parametric method; Monte Carlo simulation; scenario generation

Received on 3 October 2003. revised on 5 October 2005. accepted on 18 November 2005.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.