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Risks
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Credit Risk Management: Volume II
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Credit Risk Management: Volume II

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 21727

Special Issue Editors

Prof. Dr. Eliana Angelini

E-Mail Website
Guest Editor
Department of Economics at “G.d’Annunzio”, University of Chieti-Pescara, Viale Pindaro n. 42, 65127 Pescara, Italy
Interests: CDS; credit risk management; financial markets; ESG; bank performance; systemic risk
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Ortolano

E-Mail Website
Guest Editor
Department of Economics, Engineering, Society, Business Organization (DEIM), University of Tuscia, 01100 Viterbo, Italy
Interests: ESG; credit risk; CDS; green bonds; financial markets; banking
Special Issues, Collections and Topics in MDPI journals
Dr. Elisa Di Febo

E-Mail Website
Guest Editor
Department of Economics at “G.d’Annunzio”, University of Chieti-Pescara, Viale Pindaro n. 42, 65127 Pescara, Italy
Interests: bank performance; credit risk; CDS; financial markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Credit risk management (CRM) is one of the most critical activities that intermediaries must undertake to bear ever-growing competition in the financial industry. Credit risk management has consistently changed its characteristics over time to new aspects of financial market. Since the financial and sovereign debt crisis, the traditional framework of credit risk measurement and management has developed new aspects dealing with more systemic risk. For this reason, the impact of market and macroeconomic variables in creditworthiness assessment has grown. In this context, new challenges, including sustainability and the COVID-19 pandemic, are characterizing the analysis of credit risk over financial markets. Regarding the former, current research is focusing on ESG variables in credit risk assessment, embedding new instruments, such as environmental rating. With reference to the latter, the pandemic is causing new turmoil in the worldwide economy. In this regard, the study of the impact of this event on financial titles is essential for investors to build safe portfolios.

Authors are invited to submit papers that address these new aspects, proposing innovative empirical research for credit risk assessment and new strategies of portfolio construction in line with the perspectives of the upcoming decade.

Prof. Dr. Eliana Angelini
Dr. Alessandra Ortolano
Dr. Elisa Di Febo
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. Risks 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 factors
  • systemic risk
  • ESG
  • pandemic event study
  • sustainable portfolio construction
  • risk spillover effects
  • regulation approaches
  • bank profitability

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Published Papers (6 papers)

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Research

15 pages, 805 KiB  
Article
European Non-Performing Exposures (NPEs) and Climate-Related Risks: Country Dimensions
by Elisa Di Febo, Eliana Angelini and Tu Le
Risks 2024, 12(8), 128; https://doi.org/10.3390/risks12080128 - 13 Aug 2024
Viewed by 695
Abstract
The EU faces two economic challenges: managing non-performing exposures (NPEs) and climate change. This paper analyzes the relationship between the NPEs of domestic banking groups and climate risks, including macroeconomic variables such as the GDP growth rate, unemployment rate (UnEmp), and the voice [...] Read more.
The EU faces two economic challenges: managing non-performing exposures (NPEs) and climate change. This paper analyzes the relationship between the NPEs of domestic banking groups and climate risks, including macroeconomic variables such as the GDP growth rate, unemployment rate (UnEmp), and the voice and accountability percentile (VCA) and the interaction variable between the GHG and the Rule of Law Percentile (GhGRLP). The estimation uses ordinary least squares with time-fixed and individual effects. Physical and transition risks significantly affect NPEs, showing that both adverse climate events and the transition to a low-carbon economy worsen the financial situation of European banking institutions. The analysis also revealed that increased levels of VCA lead to a rise in NPEs, while an increase in GhGRLP reduces NPEs. In contrast, financial institutions tend to recognize and report NPEs more accurately in contexts with greater transparency and accountability. In comparison, UnEmp negatively affects NPEs, suggesting that economic support measures during high unemployment can reduce NPEs in the subsequent period. In conclusion, climate risk management represents a crucial challenge for the financial stability of banking institutions. Policymakers and financial institutions must continue to develop and implement climate change mitigation and adaptation strategies to preserve financial system stability amid growing climate uncertainties. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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30 pages, 3674 KiB  
Article
Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform
by Renatas Špicas, Airidas Neifaltas, Rasa Kanapickienė, Greta Keliuotytė-Staniulėnienė and Deimantė Vasiliauskaitė
Risks 2023, 11(7), 138; https://doi.org/10.3390/risks11070138 - 24 Jul 2023
Viewed by 1851
Abstract
It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific [...] Read more.
It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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33 pages, 1019 KiB  
Article
Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector
by Rasa Kanapickienė, Tomas Kanapickas and Audrius Nečiūnas
Risks 2023, 11(5), 97; https://doi.org/10.3390/risks11050097 - 18 May 2023
Cited by 7 | Viewed by 2344
Abstract
Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in [...] Read more.
Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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17 pages, 3623 KiB  
Article
A Non-Performing Loans (NPLs) Portfolio Pricing Model Based on Recovery Performance: The Case of Greece
by Alexandra Z. Marouli, Eugenia N. Giannini and Yannis D. Caloghirou
Risks 2023, 11(5), 96; https://doi.org/10.3390/risks11050096 - 18 May 2023
Viewed by 3642
Abstract
In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the [...] Read more.
In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the NPL portfolios considering the debtors’ behavioral response to various legal measures (phone calls, extrajudicial notices, court orders, and foreclosures). The model considered the recovery distribution over time and was applied successfully to the case of Greece. The model was also used to predict recovery, cost, and profit future cash flows, and to optimize the collection strategies related to the activation periods of different measures. A sensitivity analysis was also conducted to reveal the most significant factors affecting the collection process. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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16 pages, 3184 KiB  
Article
A Forward-Looking IFRS 9 Methodology, Focussing on the Incorporation of Macroeconomic and Macroprudential Information into Expected Credit Loss Calculation
by Douw Gerbrand Breed, Jacques Hurter, Mercy Marimo, Matheba Raletjene, Helgard Raubenheimer, Vibhu Tomar and Tanja Verster
Risks 2023, 11(3), 59; https://doi.org/10.3390/risks11030059 - 14 Mar 2023
Viewed by 9925
Abstract
The International Financial Reporting Standard (IFRS) 9 relates to the recognition of an entity’s financial asset/liability in its financial statement, and includes an expected credit loss (ECL) framework for recognising impairment. The quantification of ECL is often broken down into its three components, [...] Read more.
The International Financial Reporting Standard (IFRS) 9 relates to the recognition of an entity’s financial asset/liability in its financial statement, and includes an expected credit loss (ECL) framework for recognising impairment. The quantification of ECL is often broken down into its three components, namely, the probability of default (PD), loss given default (LGD), and exposure at default (EAD). The IFRS 9 standard requires that the ECL model accommodates the influence of the current and the forecasted macroeconomic conditions on credit loss. This enables a determination of forward-looking estimates on impairments. This paper proposes a methodology based on principal component regression (PCR) to adjust IFRS 9 PD term structures for macroeconomic forecasts. We propose that a credit risk index (CRI) is derived from historic defaults to approximate the default behaviour of the portfolio. PCR is used to model the CRI with the macroeconomic variables as the set of explanatory variables. A novice all-subset variable selection is proposed, incorporating business decisions. We demonstrate the method’s advantages on a real-world banking data set, and compare it to several other techniques. The proposed methodology is on portfolio-level with the recommendation to derive a macroeconomic scalar for each different risk segment of the portfolio. The proposed scalar is intended to adjust loan-level PDs for forward-looking information. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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14 pages, 1998 KiB  
Article
Application of the kNN-Based Method and Survival Approach in Estimating Loss Given Default for Unresolved Cases
by Aneta Ptak-Chmielewska, Paweł Kopciuszewski and Anna Matuszyk
Risks 2023, 11(2), 42; https://doi.org/10.3390/risks11020042 - 10 Feb 2023
Cited by 1 | Viewed by 2103
Abstract
A vast majority of Loss Given Default (LGD) models are currently in use. Over all the years since the new Capital Accord was published in June 2004, there has been increasing interest in the modelling of the LGD parameter on the part of [...] Read more.
A vast majority of Loss Given Default (LGD) models are currently in use. Over all the years since the new Capital Accord was published in June 2004, there has been increasing interest in the modelling of the LGD parameter on the part of both academics and practitioners. The main purpose of this paper is to propose new LGD estimation approaches that provide more effective results and include the unresolved cases in the estimation procedure. The motivation for the proposed project was the fact that many LGD models discussed in the literature are based on complete cases and mainly based on the estimation of LGD distribution or regression techniques. This paper presents two different approaches. The first is the KNN non-parametric model, and the other is based on the Cox survival model. The results suggest that the KNN model has higher performance. The Cox model was used to assign observations to LGD pools, and the LGD estimator was proposed as the average of realized values in the pools. These two approaches are quite a new idea for estimating LGD, as the results become more promising. The main advantage of the proposed approaches, especially kNN-based approaches, is that they can be applied to the unresolved cases. In our paper we focus on how to treat the unresolved cases when estimating the LGD parameter. We examined a kNN-based method for estimating LGD that outperforms the traditional Cox model. Furthermore, we also proposed a novel algorithm for selecting the risk drivers. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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