Credit Risk Modelling: Current Practices and Applications

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 2304

Special Issue Editors

Finance Department, Varna University of Economics, 9002 Varna, Bulgaria
Interests: commercial banking; corporate finance; financial management; financial literacy
Special Issues, Collections and Topics in MDPI journals
1. Faculty of Economics, Management and Accountancy, Insurance and Risk Management Department, University of Malta, MSD 2080 Msida, Malta
2. Faculty of Business, Management and Economics, University of Latvia, LV-1050 Riga, Latvia
Interests: financial technologies; financial management and asset management; risk management; compliance and regulations; corporate finance; corporate governance; audit management; financial services; behavioral economics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the Basel Committee on Banking Supervision’s 1999 report, "Credit Risk Modelling: Current Practices and Applications", credit risk modeling has remained a topic of significant interest in the field of computation. The global financial crisis, COVID-19 pandemic and growing inflationary pressures, among other factors, have provided a new impetus for the development of sophisticated credit risk modeling techniques. This poses many challenges to credit analysts, investment bankers, corporate bankers, asset managers, policy makers and the academic community. This Special Issue aims to present the latest theoretical and empirical developments in the field of measuring and modeling credit risk. We are seeking papers that explore various aspects of credit risk assessment models. Topics of interest include, but are not limited to:

  • Loan portfolio risk modeling;
  • Counterparty credit risk;
  • Regulatory oversight of credit risk models;
  • Sovereign risk;
  • Interest rate risk;
  • Stress testing;
  • Measuring credit risk: probability of default and LGD;
  • Credit scoring models;
  • Current developments of structural and reduced-form models;
  • Rating-based models;
  • Use of big data and artificial intelligence (AI) in credit risk modeling.

Dr. Dancho Petrov
Prof. Dr. Simon Grima
Prof. Dr. Inna Romānova
Guest Editors

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 submissions that pass pre-check are 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. Computation is an international peer-reviewed open access monthly 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 1800 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

  • credit risk
  • loan portfolio
  • default risk
  • probability and distance to default
  • market risk, moral hazard and adverse selection

Published Papers (1 paper)

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Research

20 pages, 334 KiB  
Article
The Impact of Financial Development and Macroeconomic Fundamentals on Nonperforming Loans among Emerging Countries: An Assessment Using the NARDL Approach
Computation 2022, 10(10), 182; https://doi.org/10.3390/computation10100182 - 11 Oct 2022
Cited by 4 | Viewed by 1535
Abstract
The relationship between financial development indicators and non-performing loans (NPLs) has garnered significant attention, especially in emerging countries. The puzzle of whether financial sector development increases or decreases Non-performing Loans (NPL)s has not been resolved to the satisfaction of the curious mind. This [...] Read more.
The relationship between financial development indicators and non-performing loans (NPLs) has garnered significant attention, especially in emerging countries. The puzzle of whether financial sector development increases or decreases Non-performing Loans (NPL)s has not been resolved to the satisfaction of the curious mind. This research attempts to answer the above question by studying the asymmetric and symmetric association between financial sector development and NPLs, by utilizing the novel non-linear autoregressive distribution lag (NARDL) and the linear autoregressive distribution lag (ARDL) approach. Moreover, to make the study inclusive, we have added a series of proxies to measure financial sector development and macroeconomic vulnerabilities. Our main findings confirm that financial sector development and NPLs move together in the long run, and there is significant evidence of the asymmetric relationship. We infer that NPLs react differently to the negative and positive shocks of financial development and macroeconomic variables both in the short and long run. In the long-run positive shocks in financial intermediation, banking efficiency, banking depth, banking stability index, and banking non-interest income significantly impact the NPLs in emerging countries. The positive shocks of financial sector development (financial intermediation and size of banks) increase NPLs in emerging countries and vice-versa. Furthermore, regarding the macroeconomic variables, the positive shock of inflation, unemployment, and interest rate positively affect NPLs. The empirical analysis also concludes that in the long-run foreign bank presence is an insignificant factor affecting NPLs in the selected countries. This study emphasizes that, unlike the linear model, the non-linear model provides a more realistic and robust result by highlighting hidden asymmetries, which will help policymakers make appropriate strategic decisions. Full article
(This article belongs to the Special Issue Credit Risk Modelling: Current Practices and Applications)
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