Elicitability and backtesting: Perspectives for banking regulation

By Natalia Nolde, J. Ziegel. 2016

199 citations recorded by the source corpus.

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.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/af532338abc0f1a53921e0d1242fbfdf37dbb9e0

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Citations in the corpus

A joint quantile and expected shortfall regression framework (2017, unknown): “…and the associated class of loss functions gives rise to a growing literature in both, joint estimation (Zwingmann and Holzmann, 2016) and in joint forecast evaluation (Acerbi and Szekely, 2014; Fissler et al., 2016; Nolde and Ziegel, 2017; Ziegel et al., 2017) for the risk measures VaR and ES.”
Forecast combinations for value at risk and expected shortfall (2020, methodology): “Backtesting VaR and ES forecasts Traditionally, VaR and ES forecasts have tended to be evaluated using tests that Nolde and Ziegel (2017) describe as unconditional and conditional calibration tests.”
Regression diagnostics meets forecast evaluation: conditional calibration, reliability diagrams, and coefficient of determination (2021, background): “Essentially, this is the setting of backtests for value-at-risk reports in banking regulation, for which we refer to (Nolde and Ziegel, 2017, Sections 2.1–2.2).”