Reverse Stress Testing: Scenario Design for Macroprudential Stress Tests

Baes, Michel; Schaanning, Eric

doi:10.2139/ssrn.3670916

Cited by 2 publications

(1 citation statement)

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“…Baes and Schaanning [24] provided an example of an algorithmic approach to solving an optimization problem similar to our own. Specifically, they sought a stress scenario that generated the worst total fire-sales loss.…”

Section: Recent Advances In Reverse Stress Testingmentioning

confidence: 99%

Operational Risk Reverse Stress Testing: Optimal Solutions

Mitic

2021

MCA

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Selecting a suitable method to solve a black-box optimization problem that uses noisy data was considered. A targeted stop condition for the function to be optimized, implemented as a stochastic algorithm, makes established Bayesian methods inadmissible. A simple modification was proposed and shown to improve optimization the efficiency considerably. The optimization effectiveness was measured in terms of the mean and standard deviation of the number of function evaluations required to achieve the target. Comparisons with alternative methods showed that the modified Bayesian method and binary search were both performant, but in different ways. In a sequence of identical runs, the former had a lower expected value for the number of runs needed to find an optimal value. The latter had a lower standard deviation for the same sequence of runs. Additionally, we suggested a way to find an approximate solution to the same problem using symbolic computation. Faster results could be obtained at the expense of some impaired accuracy and increased memory requirements.

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Section: Recent Advances In Reverse Stress Testingmentioning

confidence: 99%