Mitrodima, Gelly and Oberoi, Jaideep Singh (2024) 'CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features.' Journal of the Royal Statistical Society Series C: Applied Statistics, 73 (1). 1 -27.
![[img]](https://eprints.soas.ac.uk/style/images/fileicons/text.png) |
Text
- Accepted Version
Restricted to Repository staff only until 31 August 2024. |
Abstract
We consider alternative specifications of conditional autoregressive quantile models to estimate Value-at-Risk and Expected Shortfall. The proposed specifications include a slow moving component in the quantile process, along with aggregate returns from heterogeneous horizons as regressors. Using data for 10 stock indices, we evaluate the performance of the models and find that the proposed features are useful in capturing tail dynamics better.
| Item Type: | Journal Article |
|---|---|
| Keywords: | Value-at-Risk, Expected Shortfall, CAViaR-type models, Component models, Long range dependence |
| SOAS Departments & Centres: | Departments and Subunits > School of Finance & Management |
| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HG Finance |
| ISSN: | 00359254 |
| Copyright Statement: | This is the version of the article accepted for publication in Journal of the Royal Statistical Society Series C: Applied Statistics, published by Oxford University Press (2023). Re-use is subject to the publisher’s terms and conditions |
| DOI (Digital Object Identifier): | https://doi.org/10.1093/jrsssc/qlad081 |
| Date Deposited: | 08 Sep 2023 15:43 |
| URI: | https://eprints.soas.ac.uk/id/eprint/40227 |
Altmetric Data
Statistics
Download activity - last 12 months
Downloads since deposit
6 month trend
6 month trend
Accesses by country - last 12 months | Accesses by referrer - last 12 months |
Repository staff only
 |
Edit Item |