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

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

Deadline for manuscript submissions: 20 December 2020.

Special Issue Editor

Prof. Dr. Giampaolo Gabbi
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 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 (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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
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)
Show Figures

Figure 1

Open AccessArticle
Die Hard: Probability of Default and Soft Information
Risks 2020, 8(2), 46; https://doi.org/10.3390/risks8020046 - 13 May 2020
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
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 2
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)
Show Figures

Figure 1

Back to TopTop