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- W2904005790 abstract "We study the optimal design of scenarios by a risk-averse principal (e.g, a risk officer, a regulator) who seeks to learn about the exposures of agents (e.g., traders, banks) to a set of risk factors. We decompose the problem into a learning part and a design part. Conditional on the stress scenarios, we show how to apply a Kalman filter to solve the learning problem. The design of optimal scenarios is then a function of what the regulator wants to learn and of how she intends to intervene if she uncovers excessive exposures. We show how the optimal design depends on ex-ante leverage, the correlation of exposures within and across agents, and the non-linearities in potential losses." @default.
- W2904005790 created "2018-12-22" @default.
- W2904005790 creator A5059653804 @default.
- W2904005790 date "2022-01-01" @default.
- W2904005790 modified "2023-09-27" @default.
- W2904005790 title "Designing Stress Scenarios" @default.
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- W2904005790 doi "https://doi.org/10.2139/ssrn.4074285" @default.
- W2904005790 hasPublicationYear "2022" @default.
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