Risk Measures: Robustness, Elicitability, and Backtesting

Posted: 24 Mar 2022

See all articles by Xue Dong He

Xue Dong He

The Chinese University of Hong Kong - Department of Systems Engineering and Engineering Management

Steven Kou

Boston University

Xianhua Peng

Peking University

Date Written: March 1, 2022

Abstract

Risk measures are used not only for financial institutions’ internal risk management but also for external regulation (e.g., in the Basel Accord for calculating the regulatory capital requirements for financial institutions). Though fundamental in risk management, how to select a good risk measure is a controversial issue. We review the literature on risk measures, particularly on issues such as subadditivity, robustness, elicitability, and backtesting. We also aim to clarify some misconceptions and confusions in the literature. In particular, we argue that, despite lacking some mathematical convenience, the median shortfall—that is, the median of the tail loss distribution—is a better option than the expected shortfall for setting the Basel Accords capital requirements due to statistical and economic considerations such as capturing tail risk, robustness, elicitability, backtesting, and surplus invariance.

Suggested Citation

He, Xue Dong and Kou, Steven and Peng, Xianhua, Risk Measures: Robustness, Elicitability, and Backtesting (March 1, 2022). Annual Review of Statistics and Its Application, Vol. 9, Issue 1, pp. 141-166, 2022, Available at SSRN: https://ssrn.com/abstract=4065354 or http://dx.doi.org/10.1146/annurev-statistics-030718-105122

Xue Dong He (Contact Author)

The Chinese University of Hong Kong - Department of Systems Engineering and Engineering Management ( email )

505 William M.W. Mong Engineering Building
The Chinese University of Hong Kong, Shatin, N.T.
Hong Kong
Hong Kong

HOME PAGE: http://https://sites.google.com/site/xuedonghepage/home

Steven Kou

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States
6173583318 (Phone)

Xianhua Peng

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

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