Mitrodima, Gelly 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 |
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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: | 15 Aug 2024 19:16 |
URI: | http://eprints.lse.ac.uk/id/eprint/120880 |
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