Special Issue "Credit Risk Modeling and Management in Banking Business"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (20 December 2020).

Special Issue Editor

Prof. Dr. Giampaolo Gabbi
E-Mail Website
Guest Editor
Department of Business and Law, University of Siena; Banking and Insurance Department, SDA Bocconi School of Management, Milan, Italy
Interests: risk management; financial regulation

Special Issue Information

Dear Colleagues,

The severity of the financial crisis is largely due to the fact that the banking sectors in many countries have taken excessive risk without correspondingly increasing their capital base. Financial regulation has strengthened capital requirements, especially for credit exposures, and has explicitly addressed the dimension of the macro-prudential stability of the banking system.

At the same time, there has been a tendency to review the logic of the internal models of credit risk measurement and capital determination. Questions still remain about the ability to maintain an adequate level of bank profitability.

In the light of these important developments, this Special Issue aims to provide original contributions on credit risk management, as well as identifying new factors, methodologies, and managerial solutions for estimating the exposure of financial intermediaries, the evolution prospects of banking supervision, and the financial and real implications of the crises.

Prof. Dr. Giampaolo Gabbi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • rating
  • probability of default and recovery rates
  • credit risk internal models
  • credit risk regulation
  • credit portfolio models
  • credit derivatives and risk hedging

Published Papers (9 papers)

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Research

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Open AccessArticle
What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains
Risks 2021, 9(2), 29; https://doi.org/10.3390/risks9020029 - 25 Jan 2021
Cited by 1 | Viewed by 704
Abstract
This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset [...] Read more.
This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset from an Estonian commercial bank with 12,901 observations of defaulted and non-defaulted firms. The analysis is performed using statistical (logistic regression) and machine learning (neural networks) methods. Out of the three domains used, tax arrears show high prediction capabilities for bank loan defaults, while financial ratios and reporting delays are individually not useful for that purpose. The best default prediction accuracies were 83.5% with tax arrears only and 89.1% with all variables combined. The study contributes to the extant literature by enhancing the bank loan default prediction accuracy with the introduction of novel variables based on tax arrears, and also by indicating the pecking order of satisfying creditors’ claims in the firm failure process. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
Open AccessArticle
Measuring the Performance of Bank Loans under Basel II/III and IFRS 9/CECL
Risks 2020, 8(3), 93; https://doi.org/10.3390/risks8030093 - 02 Sep 2020
Viewed by 818
Abstract
In the last two decades, both internal and external risk management of banks have undergone significant developments. Banking supervision encourages banks to use a risk-based approach for computing minimum regulatory capital. Accounting rules have been tightened requiring more timely loss reserves for impaired [...] Read more.
In the last two decades, both internal and external risk management of banks have undergone significant developments. Banking supervision encourages banks to use a risk-based approach for computing minimum regulatory capital. Accounting rules have been tightened requiring more timely loss reserves for impaired loans. In this article, we propose a comprehensive scheme for calculating the profitability of a loan that could be used both for setting risk-based interest rates when originating a loan and for accurately determining the profitability of existing clients. The scheme utilizes the credit models developed for regulatory purposes and takes the impact of regulation on loan performance into account. We show that accounting loan loss provisions cannot be applied in a performance measurement scheme because they do not reflect the true economic loss. In addition, we demonstrate that it is crucial to measure loan performance over the full life cycle of a loan. Restricting profitability measurement to a time horizon of one year as often observed in practice could be misleading. Although our focus is on profitability measurement, the framework could be applied in a wider context, i.e., for macroeconomic stress tests, bank balance sheet projections, capital management, or evaluating the impact of securitizing parts of a bank’s loan portfolio. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
Open AccessArticle
A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes
Risks 2020, 8(2), 64; https://doi.org/10.3390/risks8020064 - 10 Jun 2020
Cited by 3 | Viewed by 1137
Abstract
This paper proposes a novel system-wide multi-state framework to model state occupations and the transitions among current, delinquency, default, prepayment, repurchase, short sale and foreclosure on mortgage loans. The approach allows for the modelling of the progression of borrowers from one state to [...] Read more.
This paper proposes a novel system-wide multi-state framework to model state occupations and the transitions among current, delinquency, default, prepayment, repurchase, short sale and foreclosure on mortgage loans. The approach allows for the modelling of the progression of borrowers from one state to another to fully understand the risks of a cohort of borrowers over time. We use a multi-state Markov model to model the transitions to and from various states. The key factors affecting the transition into various loan outcomes are the ability to pay as measured by debt-to-income ratio, equity as marked by loan-to-value ratio, interest rates and the property type. Our findings have broader policy implications for better decision-making on granting loans and the design of debt relief and mortgage modification policies. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
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Open AccessArticle
Copula-Based Assessment of Co-Movement and Tail Dependence Structure Among Major Trading Foreign Currencies in Ghana
Risks 2020, 8(2), 55; https://doi.org/10.3390/risks8020055 - 01 Jun 2020
Viewed by 823
Abstract
This paper examines the joint movement and tail dependence structure between the pair of foreign exchange rates (EUR, USD and GBP) against the GHS, using daily exchange rates data expressed in GHS per unit of foreign currencies (EUR, USD and GBP) between the [...] Read more.
This paper examines the joint movement and tail dependence structure between the pair of foreign exchange rates (EUR, USD and GBP) against the GHS, using daily exchange rates data expressed in GHS per unit of foreign currencies (EUR, USD and GBP) between the time range of 24 February 2009 and 19 December 2019. We use different sets of both static (time-invariant) and time-varying copulas with different levels of dependence and tail dependence measures, and the study results reveal positive dependence between all exchange rates pairs, though the dependencies for EUR-USD and GBP-USD pairs are not as strong as the EUR-GBP pair. The findings also reveal symmetric tail dependence, and dependence evolves over time. Notwithstanding this, the asymmetric tail dependence copulas provide evidence of upper tail dependence. We compare the copula results to DCC(1,1)-GARCH(1,1) model result and find the copula to be more sensitive to extreme co-movement between the currency pairs. The afore-mentioned findings, therefore, offer forex market players the opportunity to relax in hoarding a particular foreign currency in anticipation of domestic currency depreciation. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
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Open AccessArticle
Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks
Risks 2020, 8(2), 52; https://doi.org/10.3390/risks8020052 - 22 May 2020
Cited by 1 | Viewed by 912
Abstract
Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature [...] Read more.
Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature available indicates that research is currently limited on banks’ stress quantification in countries like India where there have been fewer failed banks. The literature also indicates a lack of scientific and quantitative approaches that can be used to predict bank survival and failure probabilities. Against this backdrop, the present study attempts to establish a bankruptcy prediction model using a machine learning approach and to compute and compare the financial stress that the banks face. The study uses the data of failed and surviving private and public sector banks in India for the period January 2000 through December 2017. The explanatory features of bank failure are chosen by using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features, and in the second step, important features are fed into the support vector machine to create a forecasting model. The threshold values of the features for the decision boundary which separates failed banks from survival banks are calculated using the decision boundary of the support vector machine with a linear kernel. The results reveal, inter alia, that support vector machine with linear kernel shows 92.86% forecasting accuracy, while a support vector machine with radial basis function kernel shows 71.43% accuracy. The study helps to carry out comparative analyses of financial stress of the banks and has significant implications for their decisions of various stakeholders such as shareholders, management of the banks, analysts, and policymakers. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
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Open AccessArticle
Die Hard: Probability of Default and Soft Information
Risks 2020, 8(2), 46; https://doi.org/10.3390/risks8020046 - 13 May 2020
Cited by 2 | Viewed by 790
Abstract
The research aims to verify whether the credit risk of small and medium-sized enterprises can be estimated more accurately using qualitative variables together with financial information from reports. In our paper, we select qualitative variables within the conceptual framework of the balanced scorecard [...] Read more.
The research aims to verify whether the credit risk of small and medium-sized enterprises can be estimated more accurately using qualitative variables together with financial information from reports. In our paper, we select qualitative variables within the conceptual framework of the balanced scorecard to assess the credit quality of Italian companies of various sizes, from micro to medium. Data were collected to estimate the company’s resilience following the shock of the financial crisis of 2007–2008. The analysis based on customer size, processes, knowledge, and corporate finance, synthesized with balanced scorecard methodology, allows us to estimate the resilience of companies in a period of crisis. The research highlights the important contribution of qualitative variables for the estimation of credit risk. The implications concern both financial intermediaries and their supervisory functions, and regulators for rating models based on soft forward and countercyclical variables. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
Open AccessArticle
Impact of Credit Risk on Momentum and Contrarian Strategies: Evidence from South Asian Markets
Risks 2020, 8(2), 37; https://doi.org/10.3390/risks8020037 - 14 Apr 2020
Cited by 1 | Viewed by 1272
Abstract
We examine the profitability of the momentum and contrarian strategies in three South Asian markets, i.e., Bangladesh, India, and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use default risk that [...] Read more.
We examine the profitability of the momentum and contrarian strategies in three South Asian markets, i.e., Bangladesh, India, and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use default risk that relates to non-payments of debts by firms as a measure of credit risk. For that purpose, we use distance to default (DD) by Kealhofer, McQuown, and Vasicek (KMV) model as a proxy of credit risk. We calculate the credit risk and form the momentum and contrarian strategies of the firms based on high, medium, and low risk. We find that in all three markets, the momentum and contrarian returns are significant for medium and high credit risk portfolios and no momentum and contrarian returns for low credit risk portfolios. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
Open AccessArticle
Credit Risk Migration and Economic Cycles
Risks 2019, 7(4), 109; https://doi.org/10.3390/risks7040109 - 29 Oct 2019
Cited by 4 | Viewed by 1145
Abstract
The misestimation of rating transition probabilities may lead banks to lend money incoherently with borrowers’ default trajectory, causing both a deterioration in asset quality and higher system distress. Applying a Mover-Stayer model to determine the migration risk of small and medium enterprises, we [...] Read more.
The misestimation of rating transition probabilities may lead banks to lend money incoherently with borrowers’ default trajectory, causing both a deterioration in asset quality and higher system distress. Applying a Mover-Stayer model to determine the migration risk of small and medium enterprises, we find that banks are over-estimating their credit risk resulting in excessive regulatory capital. This has important macroeconomic implications due to the fact that holding a large capital buffer is costly for banks and this in turn influences their ability to lend in the wider economy. This conclusion is particularly true during economic downturns with the consequence of exacerbating the cyclicality in risk capital that therefore acts to aggravate economic conditions further. We also explain part of the misevaluation of borrowers and the actual relevant weight of non-performing loans within banking portfolios: some of the prudential requirements, at least as regards EMS credit portfolios, cannot be considered effective as envisaged by the regulators who developed the “new” regulation in response to the most recent crisis. The Mover-Stayers approach helps to reduce calculation inaccuracy when analyzing the historical movements of borrowers’ ratings and consequently, improves the efficacy of the resource allocation process and banking industry stability. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
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Review

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Open AccessReview
Bank Risk Determinants in Latin America
Risks 2020, 8(3), 94; https://doi.org/10.3390/risks8030094 - 07 Sep 2020
Viewed by 806
Abstract
Systemic Banking crises are a recurrent phenomenon that affects society, and there is a need for a better understanding of the risk factors to support prudential regulation and reduce unnecessary risk intake in the financial system. This paper examines the main bank risk [...] Read more.
Systemic Banking crises are a recurrent phenomenon that affects society, and there is a need for a better understanding of the risk factors to support prudential regulation and reduce unnecessary risk intake in the financial system. This paper examines the main bank risk determinants in Latin America. The period analysed covers the timespan from 1999 to 2013, including the systemic banking crisis episodes in Argentina (2001–2003) and Uruguay (2002–2005). We apply a new data-driven comparable methodology to classify and select commercial banks from the sample. We study bank risk proxied by the Z-score. We use the system-GMM estimator as our main empirical analysis method. According to our results, well capitalized, liquid, and traditional commercial banks are less risky. We perform robustness tests by applying OLS, and the results resemble our original model. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
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