Computing the impact of central clearing on systemic risk

The paper uses a graph model to examine the effects of financial market regulations on systemic risk. Focusing on central clearing, we model the financial system as a multigraph of trade and risk relations among banks. We then study the impact of central clearing by a priori estimates in the model, stylized case studies, and a simulation case study. These case studies identify the drivers of regulatory policies on risk reduction at the firm and systemic levels. The analysis shows that the effect of central clearing on systemic risk is ambiguous, with potential positive and negative outcomes, depending on the credit quality of the clearing house, netting benefits and losses, and concentration risks. These computational findings align with empirical studies, yet do not require intensive collection of proprietary data. In addition, our approach enables us to disentangle various competing effects. The approach thus provides policymakers and market practitioners with tools to study the impact of a regulation at each level, enabling decision-makers to anticipate and evaluate the potential impact of regulatory interventions in various scenarios before their implementation.

Credit Risk Modeling Using Transfer Learning and Domain Adaptation

In the domain of credit risk assessment lenders may have limited or no data on the historical lending outcomes of credit applicants. Typically this disproportionately affects Micro, Small, and Medium Enterprises (MSMEs), for which credit may be restricted or too costly, due to the difficulty of predicting the Probability of Default (PD). However, if data from other related credit risk domains is available Transfer Learning may be applied to successfully train models, e.g., from the credit card lending and debt consolidation (CD) domains to predict in the small business lending domain. In this article, we report successful results from an approach using transfer learning to predict the probability of default based on the novel concept of Progressive Shift Contribution (PSC) from source to target domain. Toward real-world application by lenders of this approach, we further address two key questions. The first is to explain transfer learning models, and the second is to adjust features when the source and target domains differ. To address the first question, we apply Shapley values to investigate how and why transfer learning improves model accuracy, and also propose and test a domain adaptation approach to address the second. These results show that adaptation improves model accuracy in addition to the improvement from transfer learning. We extend this by proposing and testing a combined strategy of feature selection and adaptation to convert values of source domain features to better approximate values of target domain features. Our approach includes a strategy to choose features for adaptation and an algorithm to adapt the values of these features. In this setting, transfer learning appears to improve model accuracy by increasing the contribution of less predictive features. Although the percentage improvements are small, such improvements in real world lending could be of significant economic importance.

The consequence of the credit risk on the financial performance in light of COVID-19: Evidence from Islamic versus conventional banks across MEA region

Purpose

The increased number of nonperforming loans (NPLs) during COVID-19 pandemic has interrogated the robustness of banks and stability of the whole banking segment. We examine the impact of credit risk (CR) on financial performance (FP) by comparing Islamic banks (IBs) to conventional banks (CBs). We also investigate the influence of COVID-19 on this association.

Design/methodology/approach

Our sample includes the largest 200 banks across 15 countries from the Middle East and the Africa (MEA) region over a four-year period (2018–2021). Panel ordinary least squares (OLS) with fixed and random effects were used.

Findings

We find a negative association between NPLs and FP for IBs and CBs. We reveal that COVID-19 is partially mediated the association between NPLs and FP in case of the whole sample and separated sample of CBs while not in case of IBs.

Originality

The evidence of CR and FP on samples of financial sector across MEA region has not been studied in the era of COVID-19 as far as we know.

Research limitations/implications

This study contributes to the knowledge of the risk and financial performance during the crisis nexus and provides information that is valued to bankers, academics, managers and regulators for policy formulation.

SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk

In credit risk estimation, the most important element is obtaining a probability of default as close as possible to the effective risk. This effort quickly prompted new, powerful algorithms that reach a far higher accuracy, but at the cost of losing intelligibility, such as Gradient Boosting or ensemble methods. These models are usually referred to as "black-boxes", implying that you know the inputs and the output, but there is little way to understand what is going on under the hood. As a response to that, we have seen several different Explainable AI models flourish in recent years, with the aim of letting the user see why the black-box gave a certain output. In this context, we evaluate two very popular eXplainable AI (XAI) models in their ability to discriminate observations into groups, through the application of both unsupervised and predictive modeling to the weights these XAI models assign to features locally. The evaluation is carried out on real Small and Medium Enterprises data, obtained from official italian repositories, and may form the basis for the employment of such XAI models for post-processing features extraction.

Effect of Psychological Factors on Credit Risk: A Case Study of the Microlending Service in Mongolia

This paper determined the predefining factors of loan repayment behavior based on psychological and behavioral economics theories. The purpose of this research is to identify whether an individual’s credit risk can be predicted based on psychometric tests measuring areas of psychological factors such as effective economic decision-making, self-control, conscientiousness, selflessness and a giving attitude, neuroticism, and attitude toward money. In addition, we compared the psychological indicators to the financial indicators, and different age and gender groups, to assess whether the former can predict loan default prospects. This research covered the psychometric test results, financial information, and loan default information of 1118 borrowers from loan-issuing applications on mobile phones. We validated the questionnaire using confirmatory factor analysis (CFA) and achieved an overall Cronbach’s alpha reliability coefficient greater than 0.90 (α = 0.937). We applied the empirical data to construct prediction models using logistic regression. Logistic regression was employed to estimate the parameters of a logistic model. The outcome indicates that positive results from the psychometric testing of effective financial decision-making, self-control, conscientiousness, selflessness and a giving attitude, and attitude toward money enable individuals’ debt access possibilities. On the other hand, one of the variables—neuroticism—was determined to be insignificant. Finally, the model only used psychological variables proven to have significant default predictability, and psychological variables and psychometric credit scoring offer the best prediction capacities.

Financing firms in hibernation during the COVID-19 pandemic☆

The coronavirus (COVID-19) pandemic halted economic activity worldwide, hurting firms and pushing many of them toward bankruptcy. This paper discusses four central issues that have emerged in the academic and policy debates related to firm financing during the downturn. First, the economic crisis triggered by the pandemic is radically different from past crises, with important consequences for optimal policy responses. Second, it is important to preserve firms’ relationships with key stakeholders (e.g., workers, suppliers, customers, and creditors) to avoid inefficient bankruptcies and long-term detrimental economic effects. Third, firms can benefit from “hibernation,” incurring the minimum bare expenses necessary to withstand the pandemic while using credit to remain alive until the crisis subdues. Fourth, the existing legal and regulatory infrastructure is ill-equipped to deal with an exogenous systemic shock like a pandemic. Financial sector policies can help channel credit to firms, but they are hard to implement and entail different trade-offs.

Post-COVID-19 SME financing constraints and the credit guarantee scheme solution in Spain

Countries around the world are working hard to fight against the economic crisis caused by the coronavirus pandemic, with a special emphasis on small- and medium-sized enterprises (SMEs) due to their vulnerability and importance in the business ecosystem. This paper analyzes the Spanish guarantee model and the measures taken by regional governments in conjunction with the main mutual guarantee societies (MGSs) to mitigate the economic problems associated with the COVID-19 pandemic. We show that public administrations may use guarantee schemes as instruments to improve SME access to financing while limiting the burden on the public budget.

Can bank credit withstand falling house price in China?

This paper examines whether the falling house price causes credit risk or not in China. We note that bidirectional causal relationships exist in several sub-periods using sub-sample rolling window test. Our analysis confirms the option-based model (Foster & Van Order, 1984) that proves the falling house price leads to more defaults of mortgage and increasing credit risk. Meanwhile, the rise in credit risk is likely to be accompanied by the increasing house price. Rising house price has no impact on credit risk. We find banks’ credit expansion may be irrational which leads to the accumulation of credit risk. The tight credit policies and high loan interest rates stimulate the house price to fall. In addition, based on the analysis of the previous periods, we do not think that the credit risk will explode systematically in China with the current situation of mortgage growth slowing down. However, due to the pivotal role of real estate credit in loan structure of banks in China, we still need to be alert to the potential accumulation credit risk caused by the default of individuals and enterprises. It is essential for banks to strengthen the examination of personal credit certificates to prevent speculative loans. Regulators should take into account the impact on bank credit when they formulate policies to control the house price.

Maximum Entropy Methods for Loss Data Analysis: Aggregation and Disaggregation Problems

The analysis of loss data is of utmost interest in many branches of the financial and insurance industries, in structural engineering and in operation research, among others. In the financial industry, the determination of the distribution of losses is the first step to take to compute regulatory risk capitals; in insurance we need the distribution of losses to determine the risk premia. In reliability analysis one needs to determine the distribution of accumulated damage or the first time of occurrence of a composite event, and so on. Not only that, but in some cases we have data on the aggregate risk, but we happen to be interested in determining the statistical nature of the different types of events that contribute to the aggregate loss. Even though in many of these branches of activity one may have good theoretical descriptions of the underlying processes, the nature of the problems is such that we must resort to numerical methods to actually compute the loss distributions. Besides being able to determine numerically the distribution of losses, we also need to assess the dependence of the distribution of losses and that of the quantities computed with it, on the empirical data. It is the purpose of this note to illustrate the how the maximum entropy method and its extensions can be used to deal with the various issues that come up in the computation of the distribution of losses. These methods prove to be robust and allow for extensions to the case when the data has measurement errors and/or is given up to an interval.

Validation of PARX Models for Default Count Prediction

The growing importance of financial technology platforms, based on interconnectedness, makes necessary the development of credit risk measurement models that properly take contagion into account. Evaluating the predictive accuracy of these models is achieving increasing importance to safeguard investors and maintain financial stability. The aim of this paper is two-fold. On the one hand, we provide an application of Poisson autoregressive stochastic processes to default data with the aim of investigating credit contagion; on the other hand, focusing on the validation aspects, we assess the performance of these models in terms of predictive accuracy using both the standard metrics and a recently developed criterion, whose main advantage is being not dependent on the type of predicted variable. This new criterion, already validated on continuous and binary data, is extended also to the case of discrete data providing results which are coherent to those obtained with the classical predictive accuracy measures. To shed light on the usefulness of our approach, we apply Poisson autoregressive models with exogenous covariates (PARX) to the quarterly count of defaulted loans among Italian real estate and construction companies, comparing the performance of several specifications. We find that adding a contagion component leads to a decisive improvement in model accuracy with respect to the only autoregressive specification.

Factorial Network Models to Improve P2P Credit Risk Management

This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.

Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms

Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models.

Spatial Regression Models to Improve P2P Credit Risk Management

Calabrese et al. (2017) have shown how binary spatial regression models can be exploited to measure contagion effects in credit risk arising from bank failures. To illustrate their methodology, the authors have employed the Bank for International Settlements' data on flows between country banking systems. Here we apply a binary spatial regression model to measure contagion effects arising from corporate failures. To derive interconnectedness measures, we use the World Input-Output Trade (WIOT) statistics between economic sectors. Our application is based on a sample of 1,185 Italian companies. We provide evidence of high levels of contagion risk, which increases the individual credit risk of each company.