Exact inference in diagnosing value-at-risk estimates: A Monte Carlo device
AbstractIn this note a Monte Carlo approach is suggested to determine critical values for diagnostic tests of Value-at-Risk models that rely on binary random variables. Monte Carlo testing offers exact significance levels in finite samples. Conditional on exact critical values the dynamic quantile test suggested by Engle and Manganelli (2004) turns out more powerful than a recently proposed Portmanteau type test (Hurlin and Tokpavi 2006). --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. 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.
Bibliographic InfoPaper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2008,16.
Date of creation: 2008
Date of revision:
Value-at-Risk; Monte Carlo test;
Find related papers by JEL classification:
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-03 (All new papers)
- NEP-ECM-2009-01-03 (Econometrics)
- NEP-ORE-2009-01-03 (Operations Research)
- NEP-RMG-2009-01-03 (Risk Management)
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.:
- Jean-Marie Dufour, 2005.
"Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and non-standard asymptotics,"
CIRANO Working Papers
- Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
- DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 2005-03, Universite de Montreal, Departement de sciences economiques.
- Engle, Robert F & Manganelli, Simone, 1999.
"CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles,"
University of California at San Diego, Economics Working Paper Series
qt06m3d6nv, Department of Economics, UC San Diego.
- Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
- Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
- Christophe Hurlin & Sessi Tokpavi, 2006. "Backtesting VaR Accuracy: A New Simple Test," Working Papers halshs-00068384, HAL.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.