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Open AccessArticle

Modeling Expected Shortfall Using Tail Entropy

1
Department of Statistics and Econometrics, Faculty of Cybernetics, Statistics and Economic Informatics, The Bucharest University of Economic Studies, Piata Romana, nr.6, Sector 1, 010371 Bucharest, Romania
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Henley Business School, University of Reading, ICMA Centre, Whiteknights, Reading RG6 6BA, UK
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1204; https://doi.org/10.3390/e21121204
Received: 10 October 2019 / Revised: 29 November 2019 / Accepted: 5 December 2019 / Published: 7 December 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
Given the recent replacement of value-at-risk as the regulatory standard measure of risk with expected shortfall (ES) undertaken by the Basel Committee on Banking Supervision, it is imperative that ES gives correct estimates for the value of expected levels of losses in crisis situations. However, the measurement of ES is affected by a lack of observations in the tail of the distribution. While kernel-based smoothing techniques can be used to partially circumvent this problem, in this paper we propose a simple nonparametric tail measure of risk based on information entropy and compare its backtesting performance with that of other standard ES models. View Full-Text
Keywords: expected shortfall; measure of risk; information entropy; tail risk expected shortfall; measure of risk; information entropy; tail risk
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Pele, D.T.; Lazar, E.; Mazurencu-Marinescu-Pele, M. Modeling Expected Shortfall Using Tail Entropy. Entropy 2019, 21, 1204.

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