Elicitability and backtesting: Perspectives for banking regulation

Nolde, Natalia; Ziegel, Johanna F. (2017). Elicitability and backtesting: Perspectives for banking regulation. The annals of applied statistics, 11(4), pp. 1833-1874. Institute of Mathematical Statistics 10.1214/17-AOAS1041

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Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations. In order to assess forecasting performance of a risk measurement procedure, risk measure forecasts are compared to the realized financial losses over a period of time and a statistical test of correctness of the procedure is conducted. This process is known as backtesting. Such traditional backtests are concerned with assessing some optimality property of a
set of risk measure estimates. However, they are not suited to compare different risk estimation procedures. We investigate the proposal of comparative backtests, which are better suited for method comparisons on the basis of forecasting accuracy, but necessitate an elicitable risk measure. We argue that supplementing traditional backtests with comparative backtests will enhance the existing trading book regulatory framework for banks by providing the correct incentive for accuracy of risk measure forecasts. In addition, the comparative backtesting framework could be used by banks internally as well as by researchers to guide selection of forecasting methods. The discussion focuses on three risk measures, Value at Risk, expected shortfall and expectiles, and is supported by a simulation study and data analysis.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science

UniBE Contributor:

Ziegel, Johanna F.


300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 510 Mathematics
300 Social sciences, sociology & anthropology > 310 Statistics




Institute of Mathematical Statistics




Johanna Fasciati-Ziegel

Date Deposited:

20 Mar 2018 11:19

Last Modified:

05 Dec 2022 15:09

Publisher DOI:






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