Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2020
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.
Keywords
Expected Shortfall, expectiles, tail risk, risk management, tail events, tail moments
Discipline
Finance | Finance and Financial Management
Publication
Quantitative Finance
Volume
21
Issue
3
First Page
449
Last Page
460
ISSN
1469-7688
Identifier
10.1080/14697688.2020.1786151
Publisher
Taylor and Francis
Embargo Period
5-20-2022
Citation
MIHOCI, Andrija; HARDLE, Wolfgang Karl; and CHEN, Cathy Yi-Hsuan.
TERES: Tail Event Risk Expectile Shortfall. (2020). Quantitative Finance. 21, (3), 449-460.
Available at: https://ink.library.smu.edu.sg/skbi/8
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/14697688.2020.1786151