TERES: tail event risk expectile based shortfall

Mihoci, A., Haerdle, W. and Chen, C. Y.-H. (2020) TERES: tail event risk expectile based shortfall. Quantitative Finance, (doi: 10.1080/14697688.2020.1786151) (Early Online Publication)

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Abstract

We propose a generalized risk measure for expectile-based expected shortfall estimation. The generalization is designed with a mixture of Gaussian and Laplace densities. Our plug-in estimator is derived from an analytic relationship between expectiles and expected shortfall. We investigate the sensitivity and robustness of the expected shortfall to the underlying mixture parameter specification and the risk level. Empirical results from the US, German and UK stock markets and for selected NASDAQ blue chip companies indicate that expected shortfall can be successfully estimated using the proposed method on a monthly, weekly, daily and intra-day basis using a 1-year or 1-day time horizon across different risk levels.

Item Type:Articles
Additional Information:Humbold-Universität zu Berlin, Czech Science Foundation (19-28231X) and the Taiwan YuShan Scholarship, are gratefully acknowledged.
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Chen, Professor Cathy Yi-Hsuan
Authors: Mihoci, A., Haerdle, W., and Chen, C. Y.-H.
College/School:College of Social Sciences > Adam Smith Business School > Accounting and Finance
Journal Name:Quantitative Finance
Publisher:Taylor & Francis
ISSN:1469-7688
ISSN (Online):1469-7696
Published Online:02 October 2020
Copyright Holders:Copyright © 2020 Informa UK Limited, trading as Taylor & Francis Group
First Published:First published in Quantitative Finance 2020
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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