Bank Stress Testing: A Stochastic Simulation Framework

Montesi, Giuseppe; Papiro, Giovanni

doi:10.2139/ssrn.2358072

SSRN Journal

2013

DOI: 10.2139/ssrn.2358072

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Bank Stress Testing: A Stochastic Simulation Framework

Giuseppe Montesi

Giovanni Papiro²

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“…In previous research (Montesi and Papiro 2013, 2018, we described how to assess a bank's financial fragility through a stochastic simulation model by determining the frequency with which a bank may breach a regulatory capital requirement threshold (e.g., Common Equity Tier 1 ratio-CET1 ratio) in the future. This kind of exercise (i.e., assessing the probability of breach) is somewhat simpler than detecting the reverse stress test scenario, because we do not necessarily have to discover which particular adverse event combination determines a regulatory breach.…”

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Stochastic Optimization System for Bank Reverse Stress Testing

Montesi

Papiro

Fazzini³

et al. 2020

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The recent evolution of prudential regulation establishes a new requirement for banks and supervisors to perform reverse stress test exercises in their risk assessment processes, aimed at detecting default or near-default scenarios. We propose a reverse stress test methodology based on a stochastic simulation optimization system. This methodology enables users to derive the critical combination of risk factors that, by triggering a preset key capital indicator threshold, causes the bank’s default, thus detecting the set of assumptions that defines the reverse stress test scenario. This article presents a theoretical presentation of the approach, providing a general description of the stochastic framework and, for illustrative purposes, an example of the application of the proposed methodology to the Italian banking sector, in order to illustrate the possible advantages of the approach in a simplified framework, which highlights the basic functioning of the model. In the paper, we also show how to take into account some relevant risk factor interactions and second round effects such as liquidity–solvency interlinkage and modeling of Pillar 2 risks including interest rate risk, sovereign risk, and reputational risk. The reverse stress test technique presented is a practical and manageable risk assessment approach, suitable for both micro- and macro-prudential analysis.

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Stochastic Optimization System for Bank Reverse Stress Testing

Montesi

Papiro

Fazzini³

et al. 2020

JRFM

Self Cite

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Bank Stress Testing: A Stochastic Simulation Framework

Cited by 1 publication

References 6 publications

Stochastic Optimization System for Bank Reverse Stress Testing

Stochastic Optimization System for Bank Reverse Stress Testing

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