Modeling Expected Shortfall Using Tail Entropy

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, t...

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Published in:Entropy (Basel, Switzerland) Switzerland), 2019-12, Vol.21 (12), p.1204
Main Authors: Pele, Daniel Traian, Lazar, Emese, Mazurencu-Marinescu-Pele, Miruna
Format: Article
Language:English
Subjects:
Bias
Entropy
Entropy (Information theory)
Methods
Monte Carlo simulation
Regulation of financial institutions
Risk
Securities markets
Stock exchanges
Volatility
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description 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.
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subjects Bias
Entropy
Entropy (Information theory)
Methods
Monte Carlo simulation
Regulation of financial institutions
Risk
Securities markets
Stock exchanges
Volatility
title Modeling Expected Shortfall Using Tail Entropy
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