Stochastic Optimization System for Bank Reverse Stress Testing

Montesi, Giuseppe; Papiro, Giovanni; Fazzini, Massimiliano; Ronga, Alessandro

doi:10.3390/jrfm13080174

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…One further comparison is possible. We noted that some other authors have used very few Monte Carlo trials in their analyses ( [25,29], for example). Although their contexts were very different from ours, we can find what happens if we use a low number of Monte Carlo trials.…”

Section: Previous Resultsmentioning

confidence: 99%

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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: Previous Resultsmentioning

confidence: 99%

“…Montesi et al [25] implemented an RST system using simulated annealing. They said "there is no optimal algorithm to be adopted for all conditions" and did not fully justify their choice of optimization algorithm.…”

Section: Recent Advances In Reverse Stress Testingmentioning

confidence: 99%

Operational Risk Reverse Stress Testing: Optimal Solutions

Mitic

2021

MCA

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“…Therefore, the performance of banks and the current situation of their assets and liabilities are examined using different models. Numerous studies have been conducted to optimize the basic items of banks' balance sheets, all of which have been able to achieve reasonable results by considering the same objective function, i.e., maximizing the bank's profit or maximizing the difference between the bank's revenue and cost [92][93][94][95][96][97][98][99][100].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Asset and Liability Management of Banks with Minimum Possible Changes

et al. 2023

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Asset-Liability Management (ALM) of banks is defined as simultaneous planning of all bank assets and liabilities under different conditions and its purpose is to maximize profits and minimize the risks in banks by optimizing the parameters in the balance sheet. Most of the studies `and proposed models in the ALM field are based on an objective function that maximizes bank profit. It is not easy to apply changes in these models in order to reach the optimal values of the parameters in the balance sheet. In this article, an attempt has been made to propose a linear model using constraints to achieve optimal values of balance sheet parameters using ALM objectives and considering balance sheet, system and regulatory constraints. It has also been tried to design the model according to the most possible mode and with the least changes and to minimize the size of the balance sheet. The analysis of the model presented in this article has been conducted using the parameters of the balance sheet and income statement of one of the famous Iranian banks. The results obtained from the proposed model show that the values of cash and receivables from banks and other credit institutions have decreased by 30% and increased by 200%, respectively, compared to the actual values of these parameters. Also, Total Income, Operating Income and Non-Operating Income have grown by 30% compared to the actual values of these parameters. Also, the values of a number of parameters are estimated to be zero after optimization. According to the results, it is obvious that the performance of bank managers, especially in the management of bank assets, is significantly different from the optimal values of the balance sheet, and the results obtained from the proposed model can help the management of banks as much as possible.

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“…Despite the interest received from regulators, years after Grundke and Pliszka (2018), the related literature is still scarce. Montesi and Papiro (2018) and Montesi, Papiro, Fazzini, and Ronga (2020) took the pioneering step of tailoring the notion of (quantitative) RST to a bank's balance sheet by proposing a top-down approach. However, a top-down approach collapses a bank's portfolio, the risk factors driving it, its CSA terms, accounting and regulatory requirements into a handful of explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Quantitative reverse stress testing, bottom up

Albanese¹,

Crépey

Iabichino

2023

Quantitative Finance

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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.

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Stochastic Optimization System for Bank Reverse Stress Testing

Cited by 6 publications

References 26 publications

Operational Risk Reverse Stress Testing: Optimal Solutions

Operational Risk Reverse Stress Testing: Optimal Solutions

Optimization of Asset and Liability Management of Banks with Minimum Possible Changes

Quantitative reverse stress testing, bottom up

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