CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features-Reference-Cited by-同舟云学术

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

Published:2023-08-31 Issue:1 Volume:73 Page:1-27
ISSN:0035-9254
Container-title:Journal of the Royal Statistical Society Series C: Applied Statistics
language:en
Short-container-title:

Author:

Mitrodima Gelly¹,Oberoi Jaideep²

Affiliation:

1. Department of Statistics, London School of Economics and Political Science , London WC2A 2AE , UK

2. School of Finance and Management, SOAS University of London , 10 Thornhaugh Street, Russell Square, London WC1H 0XG , UK

Abstract

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.

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://academic.oup.com/jrsssc/article-pdf/73/1/1/55424157/qlad081.pdf

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