Accessibility navigation

Modelling expected shortfall using tail entropy

Pele, D. T., Lazar, E. ORCID: and Mazurencu-Marinescu-Pele, M. (2019) Modelling expected shortfall using tail entropy. Entropy, 21 (12). 1204. ISSN 1099-4300

Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

[img] Text - Accepted Version
· Restricted to Repository staff only


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.3390/e21121204


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.

Item Type:Article
Divisions:Henley Business School > ICMA Centre
ID Code:87772
Publisher:MDPI Publishing


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation