J
Johanna F. Ziegel
Researcher at University of Bern
Publications - 91
Citations - 2066
Johanna F. Ziegel is an academic researcher from University of Bern. The author has contributed to research in topics: Expected shortfall & Value at risk. The author has an hindex of 16, co-authored 75 publications receiving 1631 citations. Previous affiliations of Johanna F. Ziegel include ETH Zurich & Aarhus University.
Papers
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Coherence and elicitability
TL;DR: The existing result of the nonelicitability of expected shortfall is extended to all law-invariant spectral risk measures unless they reduce to minus the expected value.
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Higher order elicitability and Osband's principle
Tobias Fissler,Johanna F. Ziegel +1 more
TL;DR: In this paper, the authors explore the notion of elicitability for multi-dimensional functionals and give both necessary and sufficient conditions for strictly consistent scoring functions, and show that one dimensional functionals that are not elicitable can be a component of a higher order elicitable functional.
Journal ArticleDOI
Higher order elicitability and Osband’s principle
Tobias Fissler,Johanna F. Ziegel +1 more
TL;DR: In this paper, the authors explore the notion of elicitability for multi-dimensional functionals and give both necessary and sufficient conditions for strictly consistent scoring functions, and show that one dimensional functionals that are not elicitable can be a component of a higher order elicitable functional.
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Dynamic semiparametric models for expected shortfall (and Value-at-Risk)
TL;DR: This work uses recent results from statistical decision theory to overcome the problem of "elicitability" for ES by jointly modelling ES and VaR, and proposes new dynamic models for these risk measures.
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Elicitability and backtesting: Perspectives for banking regulation
Natalia Nolde,Johanna F. Ziegel +1 more
TL;DR: In this paper, the authors investigate comparative backtests, which are better suited for method comparisons on the basis of forecasting accuracy, but necessitate an elicitable risk measure, and argue that supplementing traditional backtests with comparative backtesting will enhance the existing trading book regulatory framework for banks by providing the correct incentive for accuracy of risk measure forecasts.