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

Mitrodima, Gelly ORCID: 0009-0007-5360-5221 and Oberoi, Jaideep (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. ISSN 0035-9254

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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:	Article
Official URL:	https://academic.oup.com/jrsssc
Additional Information:	© 2023 The Royal Statistical Society
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
JEL classification:	C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice; Investment Decisions
Date Deposited:	28 Nov 2023 12:15
Last Modified:	12 Dec 2024 03:58
URI:	http://eprints.lse.ac.uk/id/eprint/120880

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