Abstract
ARCH and GARCH models are widely used to model financial market volatilities in risk management applications. Considering a GARCH model with heavy-tailed innovations, we characterize the limiting distribution of an estimator of the conditional value-at-risk (VaR), which corresponds to the extremal quantile of the conditional distribution of the GARCH process. We propose two methods, the normal approximation method and the data tilting method, for constructing confidence intervals for the conditional VaR estimator and assess their accuracies by simulation studies. Finally, we apply the proposed approach to an energy market data set. © 2006 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 556-576 |
| Journal | Journal of Econometrics |
| Volume | 137 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 2007 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Data tilting
- GARCH models
- Heavy tail
- Tail empirical process
- Value-at-risk
Fingerprint
Dive into the research topics of 'Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver