A Top-down Approach to Stress-testing Banks

Kapinos, Pavel S.; Mitnik, Oscar A.

doi:10.1007/s10693-015-0228-8

Cited by 51 publications

(10 citation statements)

References 22 publications

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“…Although traditional CAMELS indicators are found to be successful in anticipating bank failures in the U.S., Canbas et al (2005) found that these criteria did not maintain a oneto-one correspondence with the specific financial characteristics for Turkish commercial banks due to different applications of bank regulatory and supervisory actions. Kapinos and Mitnik (2016) proposed a simple method for stress-testing banks using a top-down approach that captured the heterogeneous impact of shocks to macroeconomic variables on banks' capitalization. They performed a principal component analysis on the selected variables and showed how the principal component factors can be used to make projections, conditional on exogenous paths of macroeconomic variables.…”

Section: Review Of Predicting Indicatorsmentioning

confidence: 99%

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Liu

Sathye

2021

JRFM

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Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research.

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Section: Review Of Predicting Indicatorsmentioning

confidence: 99%

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Liu

Sathye

2021

JRFM

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“…Based on the results of our analysis, which accounts for the heterogeneity of banks as in for example, Kapinos and Mitnik (2016), presented in Table 5, the small banks exhibit the highest TCR and CAP (the bank capital to assets ratio), which is supported by the lowest probability of failing, while their beta, expected loss, and full cost are intermediate between those of the medium and large banks (similar to e.g., Capponi et al, 2017; DeYoung et al, 2018; Leanza et al, 2021). In the case of the large banks, one observes the highest probability of failure and beta and the lowest capital ratios, expected losses and full cost.…”

Section: Resultsmentioning

confidence: 99%

“The Crooked Smile of TCR”: Banks’ Solvency and Restructuring Costs in the European Banking Industry

2021

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In this study, we analyze the probability of bank failure, the expected losses, and the costs of bank restructuring with the application of a lognormal distribution probability function for three categories of European banks, that is, small, medium, and large, over the post-crisis period from 2012 to 2016. Our goal was to determine whether the total capital ratio (TCR) properly reflects banks’ solvency under stress conditions. We identified a phenomenon that one can call the “crooked smile of TCR”. Medium-sized banks with relatively high TCRs performed poorly in stress tests; however, the probability of bank failure increases slightly with the size of the bank, while the TCR decreases. We claim that the focus on capital adequacy measures is not sufficient to achieve the goal of improving banks’ stability and reducing their restructuring costs. Our results are of special importance for medium-sized banks, as these banks are not regularly subjected to publicly available stress tests.

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“…The study found that the results become more accurate when the additional variables are included. Kapinos and Mitnik (2016) found that the top-down approach to stress testing banks in the USA can be used to examine the banks solvency. Cleary and Hebb (2016) examined the failures of 132 American banks during the period 2002-2009 using discriminant analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Early Warning of Bank Failure in the Arab Region: A Logit Regression Approach

Obeid

2022

AJEER

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The global financial crisis of 2008 taught the biggest lesson of anticipating a financial crisis. The current study aimed to highlight the importance of central banks to build early warning systems to reduce the costs of resolution procedures of weak banks. The data was obtained from published annual reports and balance sheets of 60 commercial banks in the Arab region for the period 2000-2010. Using the logistic regression model to predict the performance of banks or anticipating the possibility of bank failure and build an early warning system, the study identified a few financial indicators such as Capital Adequacy Ratio (CAR); Liquidity (LIQ); Cost to Income Ratio CIR; Return On. Assets (ROA); and Non-Performing Loans (NPL). The impact of the GDP variable on bank`s failure was also determined to capture economic risks. The results showed that financial soundness indicators (FSI) can be used efficiently to predict bank failure, that the variables of ROA and CAR had the greatest impact on the probability of the bank’s survival, while no statistical significance was seen for the GDP variable. The paper recommends the importance of the financial stability and banking supervision departments to build early warning systems. The study would provide useful insights to both household and corporate sectors to look for early warning signs that predict the performance of the banking sector in the Arab countries. The FSIs suggested in the study would also play a prominent role in predicting the success or failure of banks in the Arab region.

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A Top-down Approach to Stress-testing Banks

Cited by 51 publications

References 22 publications

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

“The Crooked Smile of TCR”: Banks’ Solvency and Restructuring Costs in the European Banking Industry

Early Warning of Bank Failure in the Arab Region: A Logit Regression Approach

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