Why Risk Is So Hard to Measure

29 Pages Posted: 23 Apr 2015 Last revised: 1 Jul 2016

See all articles by Jon Danielsson

Jon Danielsson

London School of Economics - Systemic Risk Centre

Chen Zhou

De Nederlandsche Bank; Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)

Date Written: June 22, 2016

Abstract

This paper analyzes the robustness of standard techniques for risk analysis, with a special emphasis on the Basel III risk measures. We focus on the difference between value-at-risk and expected shortfall, their small sample properties, the scope for manipulating risk measures and how estimation can be improved. Overall, the paper find that risk forecasts are extremely uncertain at low sample sizes, with value-at-risk more accurate than expected shortfall, while value-at-risk is easily manipulated without violating regulations. Finally the implications for practitioners and regulators are discussed along with best practice suggestions.

Keywords: Value-at-Risk, expected shortfall, finite sample properties, Basel III

JEL Classification: C10, C15, G18

Suggested Citation

Danielsson, Jon and Zhou, Chen, Why Risk Is So Hard to Measure (June 22, 2016). De Nederlandsche Bank Working Paper No. 494. Available at SSRN: https://ssrn.com/abstract=2597563 or http://dx.doi.org/10.2139/ssrn.2597563

Jon Danielsson (Contact Author)

London School of Economics - Systemic Risk Centre ( email )

Houghton Street
London WC2A 2AE
United Kingdom
+44.207.955.6056 (Phone)

HOME PAGE: http://www.riskreasearch.org

Chen Zhou

De Nederlandsche Bank ( email )

PO Box 98
1000 AB Amsterdam
Amsterdam, 1000 AB
Netherlands

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

Register to save articles to
your library

Register

Paper statistics

Downloads
945
Abstract Views
3,278
rank
23,474
PlumX Metrics