A Top-Down Approach to the Stress-Testing of Banks

Kapinos, Pavel S.; Mitnik, Oscar A.

doi:10.2139/ssrn.2469425

Cited by 20 publications

(35 citation statements)

References 21 publications

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“…iables were similar to Kapinos and Mitnik (2016), but expanded by government debt to GDP, private sector credit flow to GDP and international investment position to GDP. The list of banking sector variables included e.g., total assets to GDP, debt to equity, loans to deposits, mortgages to loans, non-core liabilities.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 97%

“…Therefore, our review of literature is selective and focuses on the studies which accounted for the impact of macroeconomic environment on bank distress. To the best of our knowledge, there are two studies fulfilling these criteria: Kapinos and Mitnik (2016) for the US banks and Betz et al (2014) for European banks. Kapinos and Mitnik (2016) conducted stress tests for medium and large banks showing how resilient individual banks and banking sector are to macroeconomic shocks.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

“…To the best of our knowledge, there are two studies fulfilling these criteria: Kapinos and Mitnik (2016) for the US banks and Betz et al (2014) for European banks. Kapinos and Mitnik (2016) conducted stress tests for medium and large banks showing how resilient individual banks and banking sector are to macroeconomic shocks. They applied principal component analysis (PCA) to macroeconomic factors and least absolute shrinkage and selection operator (LASSO) combined with PCA for bank-level characteristics.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

“…the pre-provision net revenue and net charge-offs on loans and leases, both representing earnings. The goal of Kapinos and Mitnik (2016) was to run stress tests and therefore we cannot follow their methodology in this study, but we apply similar list of macroeconomic and bank-level variables adjusted to the availability of data and specifics of European accounting. Betz et al (2014) based on quarterly data of European banks from 2000 to mid-2013 used CAMEL-like bank-level variables and macroeconomic and banking sector variables.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

“…H1 is motivated by positive results of Betz et al (2014) and the role of macroeconomic factors for banks financial capacity confirmed by Kapinos and Mitnik (2016). H2 is motivated by the fact that studies on European bank distress, due to the limited number of distress cases, did not account for heterogeneity of countries.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

See 4 more Smart Citations

Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

Iwanicz-Drozdowska

Ptak-Chmielewska

2019

Folia Oeconomica

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In this study we focus on distress events of European banks over the period of 1990-2015, using unbalanced panel of 3,691 banks. We identify 132 distress events, which include actual bankruptcies as well as bailout cases. We apply CAMEL-like bank-level variables and control macroeconomic variables (GDP, inflation, unemployment rate). The analysis is based on traditional logistic regression and k-means clustering. We find, that the probability of distress is connected with macroeconomic conditions via regional grouping (clustering). Bank-level variables that were stable predictors of distress from 1 to 4 years prior to event are equity to total assets ratio (leverage) and loans to funding (liquidity). From macroeconomic factors, the GDP growth is a reasonable variable, however with differentiated impact: for 1 year distance high distress probability is connected with low GDP growth, but for 2, 3 and 4 year distance: high distress probability is conversely connected with high GDP growth. This shows the changing role of macroeconomic environment and indicates the potential impact of favorable macroeconomic conditions on building-up systemic problems in the banking sector.FOE 6(345) 2019 www.czasopisma.uni.lodz.pl/foe/ ale dla 2, 3 i 4 lat przed bankructwem wysokie ryzyko bankructwa jest związane z wysoką dynamika PKB, czyli jest to zależność odwrotna. Pokazuje to zmienną rolę otoczenia makroekonomicznego i wskazuje na potencjalny wpływ sprzyjających warunków makroekonomicznych na powstawanie problemu systemowego w sektorze bankowym.Słowa kluczowe: bankructwo, CAMEL, predykcja logistyczna, zmienne makroekonomiczne JEL: G20, G21

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Section: Literature Review and Research Hypothesismentioning

confidence: 97%

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

Iwanicz-Drozdowska

Ptak-Chmielewska

2019

Folia Oeconomica

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Interest rate uncertainty and the predictability of bank revenues

Çepni

Demirer

Gupta

et al. 2022

Journal of Forecasting

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This paper examines the predictive power of interest rate uncertainty over preprovision net revenues (PPNR) in a large panel of bank holding companies (BHC). Utilizing a linear dynamic panel model, we show that supplementing forecasting models with interest rate uncertainty improves the forecasting performance with the augmented model yielding lower forecast errors in comparison to a baseline model which includes unemployment rate, federal funds rate, and spread variables. Further separating PPNRs into two components that reflect net interest and noninterest income, we show that the predictive power of interest rate uncertainty is concentrated on the non-interest component of bank revenues. Finally, examining the point predictions under a severely stressed scenario, we show that the model can successfully predict the negative effect on overall bank revenues with a rise in the non-interest component of income during 2009:Q1. Overall, the findings suggest that stress testing exercises that involve bank revenue models can benefit from the inclusion of interest rate uncertainty and the cross-sectional information embedded in the panel of BHCs.

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Forecasting bank leverage: an alternative to regulatory early warning models

Hambusch

Shaffer

2016

J Regul Econ

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Bank regulators have worked to develop statistical models predicting bank failures, but such models cannot be estimated during periods of few failures. We address this problem using an alternative approach, forecasting the leverage ratio as a continuous variable that avoids the small sample problem. The leverage ratio is a natural choice in this setting both because of its historically consistent ability to predict failures and because of regulators' primary focus on bank capitalization. Our model selection draws on both the earlier literature and more recent stress-testing studies. Out-of-sample performance shows promise as a supplement to the standard approach.

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A Top-Down Approach to the Stress-Testing of Banks

Cited by 20 publications

References 21 publications

Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

Interest rate uncertainty and the predictability of bank revenues

Forecasting bank leverage: an alternative to regulatory early warning models

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