CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features

Mitrodima, Gelly; Oberoi, Jaideep

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.

Citation

Mitrodima, G., & Oberoi, J. (2024). CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features. Journal of the Royal Statistical Society: Series C, 73(1), 1 -27. https://doi.org/10.1093/jrsssc/qlad081

• Journal Article Type: Article
• Acceptance Date: Aug 7, 2023
• Online Publication Date: Aug 31, 2023
• Publication Date: Jan 1, 2024
• Deposit Date: Sep 8, 2023
• Publicly Available Date: Sep 8, 2023
• Print ISSN: 0035-9254
• Electronic ISSN: 1467-9876
• Publisher: Royal Statistical Society
• Peer Reviewed: Peer Reviewed
• Volume: 73
• Issue: 1
• Pages: 1 -27
• DOI: https://doi.org/10.1093/jrsssc/qlad081
• Keywords: Value-at-Risk, Expected Shortfall, CAViaR-type models, Componentmodels, Long range dependence