This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Extreme Returns, Tail Estimation, and Value-at-Risk

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Jon Danielsson ()

Additional information is available for the following registered author(s):

Abstract

Accurate prediction of extreme events are of primary importance in many financial applications. The properties of historical simulation and Risk Metrics techniques for computing Valu-at Risk (VaR) are compared with a method which involves modelling the tails of financial returns explicitly with a tail estimator. The methods are compared using a sample of U.S. stock returns. For predictions of low probability worst outcomes, RiskMetrics type analysis underpredicts while historical simulation overpredicts. However, the estimates obtained from applying the tail estimator are more accurate in the VaR prediction. This implies that capital requirements can be lower by doing VaR with the tail estimator.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://fmg.lse.ac.uk/pdfs/dp273.pdf
File Format: application/pdf
File Function:
Download Restriction: Financial Markets Group Working Papers are free to download for academics and students, and for our subscribers and sponsors. If you fall into one of these categories but have trouble downloading our papers, or if you do not fall into one of these categories but would like to pay for a copy, please contact us at fmg@lse.ac.uk

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Publisher Info
Paper provided by Financial Markets Group in its series FMG Discussion Papers with number dp273.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: Jul 1997
Date of revision:
Handle: RePEc:fmg:fmgdps:dp273

Contact details of provider:
Web page: http://fmg.lse.ac.uk/

For technical questions regarding this item, or to correct its listing, contact: (The FMG Administration).

Related research
Keywords:

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Andreas Gottschling & Christian Haefke & Halbert White, 1999. "Closed Form Integration of Artificial Neural Networks with Some Applications to Finance," University of California at San Diego, Economics Working Paper Series 1999-24, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
  2. Peter F. Christoffersen & Francis X. Diebold, 1997. "How Relevant is Volatility Forecasting for Financial Risk Management?," Center for Financial Institutions Working Papers 97-45, Wharton School Center for Financial Institutions, University of Pennsylvania. [Downloadable!]
    Other versions:
  3. Straetmans, Stefan, 2000. "Extremal spillovers in financial markets," Serie Research Memoranda 0013, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics. [Downloadable!]
  4. Xiongwei Ju & Neil D. Pearson, 1998. "Using Value-at-Risk to Control Risk Taking: How Wrong Can you Be?," Finance 9810002, EconWPA. [Downloadable!]
Statistics
Access and download statistics

Did you know? RePEc also has a blog.

This page was last updated on 2009-11-16.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.