Quotation Fissler, Tobias, Ziegel, Johanna. 2021. Evaluating Range Value at Risk Forecasts. SIAM 2021 Conference on Financial Mathematics and Engineering, Virtual Conference, United States/USA, 01.06.-04.06.




The debate of what quantitative risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) and Expected Shortfall (ES). Range Value at Risk (RVaR) is a natural interpolation between VaR and ES, constituting a tradeoff between the sensitivity of ES and the robustness of VaR, turning it into a practically relevant risk measure on its own. Hence, there is a need to statistically assess, compare and rank the predictive performance of different RVaR models, tasks subsumed under the term 'comparative backtesting’ in finance. This is best done in terms of strictly consistent loss or scoring functions, i.e., functions which are minimised in expectation by the correct risk measure forecast. Much like ES, RVaR does not admit strictly consistent scoring functions, i.e., it is not elicitable. Mitigating this negative result, we show that a triplet of RVaR with two VaR-components is elicitable. We characterise all strictly consistent scoring functions for this triplet. Additional properties of these scoring functions are examined, including the diagnostic tool of Murphy diagrams. The results are illustrated with a simulation study, and we put our approach in perspective with respect to the classical approach of trimmed least squares regression. This talk is based on joined work with Johanna F. Ziegel (preprint available at https://arxiv.org/abs/1902.04489)


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Evaluating Range Value at Risk Forecasts
Event SIAM 2021 Conference on Financial Mathematics and Engineering
Year 2021
Date 01.06.-04.06
Country United States/USA
Location Virtual Conference
URL https://www.siam.org/conferences/cm/conference/fm21


Fissler, Tobias (Details)
Ziegel, Johanna (University of Bern, Switzerland)
Institute for Statistics and Mathematics IN (Details)
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