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Model risk

Quantitative reverse stress testing, bottom up

Posted by on 29 November 2022
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In august we published the first out of three papers co-authored by Stéphane Crépey, Université Paris Cité / Laboratoire de Probabilités, Statistique et Modélisation (LPSM). The first paper covered a darwinian theory of model risk and puts forward an ex ante methodology to analyse this pattern for the broad class of structures, whereby a dealer buys long-term convexity from investors and resells hedges to be used for risk management purposes.  We are delighted to be able to published the second paper on this theme, concerning reverse stress testing. Read the abstract here and download the full paper below.

We propose a bottom-up quantitative reverse stress testing framework that identifies forward-looking fragilities tailored to a bank’s portfolio, credit and funding strategies, models, and calibration constraints. Thus, instead of relying on historical events, we run a Monte Carlo simulation, and we mine those future states that contribute the most to a bank’s cost of capital expressed in terms of scenario differential. We find that such an approach allows identifying both the systemic and idiosyncratic weaknesses of the bank’s portfolio, with applications that include solvency risk, extreme events hedging, liquidity risk management, trading and credit limits, model validation and model risk management.

Please download the full paper here.

This paper has been submitted to the journal Quantitative Finance.

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