Forecasting Bank Default with the Merton Model: The Case of US Banks

Jo, Kihwan; Choi, Gahyun; Jeong, Jongwook; Ahn, Kwangwon

doi:10.1007/978-3-031-08751-6_49

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Moreover, a Z-score is an accounting-based indicator of default risk [83,[93][94][95][96][97][98][99][100][101][102][103]. Alternatively, there is a market-based metric based on Merton's structural distance-to-default model [104][105][106][107][108][109][110][111][112][113]. Studies also attempt to assess the value of the government's financial safety net to shareholders as the value of a put option by taxpayers, which is another way risk shifting may be quantified [114][115][116][117][118][119][120][121][122].…”

Section: Literature Reviewmentioning

confidence: 99%

Bank Risk Literature (1978–2022): A Bibliometric Analysis and Research Front Mapping

Marie

Abdelwahed

et al. 2023

Sustainability

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This study maps the conceptual structure of the body of knowledge concerning bank risk to understand this research strand better. A bibliometric analysis including 671 publications from January 1978 to October 2022 was conducted to achieve the aim of the study. The analysis of descriptive indicators identifies the main traits of scholars debating bank risk in terms of the annual production of publications; most productive authors, countries, affiliations, and journals; and most cited articles in the dataset. This study performs a co-word analysis by adopting social network analysis tools to analyze the conceptual structure of the dataset. The results highlight growing academic interest in bank risk research topics, especially following the global financial crisis. The bibliometric analysis reveals three main topics concerning the consideration of bank risk: (1) the adoption of risk management and bank risk, (2) the use of bank risk during the financial crisis, and (3) the interrelations between corporate governance and bank risk.

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Section: Literature Reviewmentioning

confidence: 99%

Bank Risk Literature (1978–2022): A Bibliometric Analysis and Research Front Mapping

Marie

Abdelwahed

et al. 2023

Sustainability

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“…To summarize, extant studies investigate bank risk-taking behavior, but the proxy variables used differ largely. Existing research use proxies such as volatility of ROA ( García-Kuhnert et al., 2015 ; Mourouzidou-Damtsa et al., 2019 ), risk assets-to-total assets ratio ( Delis and Kouretas, 2011 ), regulatory capital measured by core capital ratios, total risk-based capital ratios, and equity-to-total asset ratios ( Vodová, 2019 ; Aldasoro et al., 2022 ), credit risk measures such as impaired loan ratios, non-performing loan-to-total asset ratios, and loan loss reserve ratios (see Mio et al., 2022 ), insolvency risk measured by z-scores ( Aljughaiman and Salama, 2019 ; Teixeira et al., 2020 ; Mio et al., 2022 ), the CDS spread ( Drago et al., 2019 ; Di Tommaso and Thornton, 2020 ), and Merton's distance-to-default (DD) ( Chiaramonte et al., 2021 ; García et al., 2022 ; Jo et al., 2022 ).…”

Section: Literature and Hypothesis Developmentmentioning

confidence: 99%

Is bank risk appetite relevant to bank default in times of Covid-19?

Lee

Lye

Lee

2022

Central Bank Review

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On predicting the probability of default of credit organizations in the Russian Federation

Zakirova

2024

RusJEL

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Objective: to form a model for predicting the default of credit organizations under the modern conditions of the banking sector functioning.Methods: unidimensional analysis of variance, regression analysis of binary choice models.Results: in the modern economy, the banking system stability largely affects not only the financial sector, but also the economic and investment climate in the country. Understanding of the banks’ influence on the economy necessitates the formation of appropriate effective forecasting systems that allow identifying problem banks before revoking their licenses is necessary. The existing methodology of the Bank of Russia is characterized by subjectivity and inaccuracy of assessment. The analysis of studies on predicting bank defaults showed various approaches to the methodology of assessing the probability of credit institutions’ bankruptcy, though they have a number of shortcomings. Based on the selection of key factors affecting the bank’s financial stability, the logistic regression model for predicting bankruptcy of banks was formed. The methodology proposed in this article includes five predictors, selected on the basis of the improved methodology for selecting logit regression variables, and complements the existing methodologies.Scientific novelty: a methodology for assessing the probability of commercial banks’ bankruptcy in the Russian Federation was developed, which includes five key predictors for assessing the bank’s financial stability: return on assets, unit weight of liquid assets in the balance sheet currency, unit weight of the loan portfolio in the balance sheet currency, share of loans to the real sector in the balance sheet currency, and share of long-term placements in the loan portfolio. The logistic regression model of binary choice proposed in the paper allows distinguishing financially stable credit organizations from problem banks with a forecasting horizon of five months and a classification accuracy of 88,33 %.Practical significance: the relatively high classification accuracy of the model allows its use by the Bank of Russia in controlling the credit organizations functioning, as well as directly by the credit organization’s management, in order to assess the organization’s financial stability and to predict the default probability, as well as to form the bank’s development strategy.

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Forecasting Bank Default with the Merton Model: The Case of US Banks

Cited by 3 publications

References 13 publications

Bank Risk Literature (1978–2022): A Bibliometric Analysis and Research Front Mapping

Bank Risk Literature (1978–2022): A Bibliometric Analysis and Research Front Mapping

Is bank risk appetite relevant to bank default in times of Covid-19?

On predicting the probability of default of credit organizations in the Russian Federation

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