Skip Navigation


Journal of Financial Econometrics Advance Access originally published online on February 9, 2008
Journal of Financial Econometrics 2008 6(2):231-252; doi:10.1093/jjfinec/nbn001
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
6/2/231    most recent
nbn001v1
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 Taylor, J. W.
Right arrow Search for Related Content
Related Collections
Right arrow C22 - Time-Series Models
Right arrow C53 - Forecasting and Other Model Applications
Right arrow C10 - General
Right arrow G14 - Information and Market Efficiency; Event Studies
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.

Estimating Value at Risk and Expected Shortfall Using Expectiles

James W. Taylor
     Saïd Business School, University of Oxford

Address for correspondence: Saïd Business School, University of Oxford, Park End Street, Oxford OX1 1HP, UK. Tel.: +44 (0)1865 288927; Fax: +44 (0)1865 288805; email: james.taylor{at}sbs.ox.ac.uk

JEL Classification: C22, C53, C10


  Abstract

Expectile models are derived using asymmetric least squares. A simple formula has been presented that relates the expectile to the expectation of exceedances beyond the expectile. We use this as the basis for estimating the expected shortfall. It has been proposed that the {theta} quantile be estimated by the expectile for which the proportion of observations below the expectile is {theta}. In this way, an expectile can be used to estimate value at risk. Using expectiles has the appeal of avoiding distributional assumptions. For univariate modeling, we introduce conditional autoregressive expectiles (CARE). Empirical results for the new approach are competitive with established benchmarks methods.

KEYWORDS: financial risk, asymmetric least squares, expectiles, conditional autoregressive expectiles

I am grateful for the helpful comments of Jan de Gooijer and Keming Yu. I am also grateful for the insightful comments of an associate editor and two referees.


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


This article has been cited by other articles:


Home page
 JOURNAL OF FINANCIAL ECONOMETRICSHome page
J. W. Taylor
Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall
J. Financial Econometrics, July 1, 2008; 6(3): 382 - 406.
[Abstract] [Full Text] [PDF]


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.