Credit Risk Measurement Research Articles

Does Bitcoin Hedge Industry Credit Risk? A Comparison with Gold

Credit default swaps are considered indicators of default probability and used to measure credit risk in different sectors of the US industry. This study examines the effectiveness of hedging and safehaven options for US sectoral credit default swap indices, focusing on whether Bitcoin or gold can serve as effective assets for mitigating credit risk in US industries. The GARCH model with dummy variables and quantile regression are employed to estimate the hedging and safe-haven properties of Bitcoin and gold. The findings indicate that both Bitcoin and gold can be utilized as effective hedging and safe haven assets for US industry credit risk. Furthermore, the study highlights the superior hedging potential and safe-haven properties of Bitcoin compared to gold. Overall, the results suggest that investors and portfolio managers can effectively utilize Bitcoin and gold to protect against credit risk in different US sectors, regardless of market and economic conditions.

Microcredit Pricing Model for Microfinance Institutions under Basel III Banking Regulations

Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit.

Contagion network, portfolio credit risk, and financial crisis

We develop a network-based nonlinear dynamical system model to study credit exposure risk at the portfolio level. This model captures the complex characteristics of contagion arising from the microstructural interdependencies among firms, especially looping effects. We find that when contagion features are not adequately modeled, portfolio credit risk is generally underestimated, but becomes overestimated during a crisis. This can partly explain the outbreak and subsequent intensification of a crisis such as the 2008 financial crisis. We also derive an expression for the portfolio loss of a regular network, which has implications for measurement and pricing of portfolio credit risk.

A Prediction Model for Credit Risk Measurement of Small and Micro Enterprises Based On Particle Swarm Optimization random forest algorithm

In the context of rapid economic development, the credit risk assessment of small and micro enterprises has become the focus of attention in the financial field. The random forest algorithm is widely used in credit risk assessment due to its high accuracy and robustness, but it has some problems, such as difficulty in parameter selection and performance degradation in processing unbalanced datasets. In this study, we propose a credit risk prediction model for small and micro enterprises based on Particle Swarm Optimization Random Forest Algorithm (PSO-RF). The particle swarm optimization effectively solves the problem of random forest parameter selection and improves the prediction performance of the model through the balance of global search and local search. Experimental results show that the PSO-RF algorithm shows significant performance advantages in credit risk prediction, performs well in dealing with unbalanced datasets, and has certain advantages in feature selection. This study provides new ideas and methods for the credit risk assessment of small and micro enterprises, and is of great significance for improving the risk management capabilities of financial institutions and supporting the healthy development of small and micro enterprises.

Application of KMV Model in Credit Risk Management in Banking Industry

As an important part of the national financial system, the banking industry bears important responsibilities for the national economy. In addition, the banking industry is considered an area full of high risks. The risk management of banks not only involves the national finance, economy and national security, but also directly affects the national financial system and the stability of the global financial order. Credit risk has always been one of the risks that domestic and foreign banking banks pay special attention to. Effectively identifying and preventing credit risk is the key to ensure the smooth operation of banks. In order to better identify and manage the credit risk of the listed banks in China, this study uses the KMV model to measure the default distance of the sample banks. Will default distance as assessment of China listed commercial Banks credit risk agent variables, using before 2019 in China's a-share market listed commercial Banks build panel data, empirical analysis, and discuss the analysis results, put forward relevant policy Suggestions, model support for the healthy and steady development of the banking industry. This paper analyzes the current situation of the application of modern credit risk measurement model in the risk management of the banking industry. Meanwhile, combining with the data of some listed banks in China, discusses the credit risks and challenges faced by banks through in-depth analysis, and provides development suggestions with the data analysis of financial model.

INVESTIGATING THE IMPACT OF CREDIT RISK ON FINANCIAL PERFORMANCE OF COMMERCIAL BANK IN INDIA

In this liberalization period, credit Risk Management has got much importance in the Indian Economy. The main challenges faced by the banking sector today are the challenge of identifying the risk and managing it. The risk is imbibed nature of the banking business. The main role of a bank is of intermediate for those having resources and requiring resources. For risk management various risks like credit risk, market risk or operational risk have to be converted into one composite measure. The importance of credit risk management and its impact on profitability has motivated us to pursue this study. We assume that if the credit risk management is sound, the profit level will be satisfactory. The other way around, if the credit risk management is poor, the profit level will be relatively lower. Because the less the banks loss from credits, the more the banks gain. Therefore, it is necessary that measurement of credit risk should be in tandem with other measurements of operation and market risk so that the requisite composite estimate can be worked out. So, in banking sector credit risk management is being most important task of all. Moreover, the central question is how significant the impact of credit risk management on profitability is. This thesis is an endeavor to find the answer. The principal concern of this thesis is to ascertain to what extent banks can manage their credit risks, what tools or techniques are at their disposal and to what extent their performance can be augmented by proper credit risk management policies and strategies.

Research on Credit Risk Measurement and Management of Commercial Banks in China

This comprehensive study explores the sophisticated landscape of credit risk measurement and management within the commercial banking sector of China, a critical component in maintaining the resilience and stability of its burgeoning financial system. As China's economy continues to integrate with the global market, its banking sector faces escalating credit risk challenges, necessitating advanced and effective management strategies. This paper critically examines the existing methodologies for measuring credit risk, including both traditional approaches and modern innovations that leverage big data and artificial intelligence, assessing their applicability and efficiency in the Chinese context. Moreover, we delve into the multifaceted strategies employed by Chinese commercial banks to manage credit risk, focusing on the integration of regulatory frameworks, technological advancements, and best practices in risk management. Through a methodical review of literature and case studies of prominent Chinese banks, this study identifies key trends, challenges, and opportunities in credit risk management. We evaluate the effectiveness of current practices and propose a set of strategic recommendations aimed at enhancing the robustness of credit risk management systems. These include adopting a more holistic approach to risk assessment, leveraging technology to predict and mitigate risk, and fostering a culture of risk awareness and compliance.

Geographic Disaggregation of House Price Stress Paths: Implications for Single-Family Credit Risk Measurement

Geographic Disaggregation of House Price Stress Paths: Implications for Single-Family Credit Risk Measurement

A static replication approach for callable interest rate derivatives: mathematical foundations and efficient estimation of SIMM–MVA

The computation of credit risk measures such as exposure and Credit Value Adjustments (CVA) requires the simulation of future portfolio prices. Recent metrics, such as dynamic Initial Margin (IM) and Margin Value Adjustments (MVA) additionally require the simulation of future conditional sensitivities. For portfolios with non-linear instruments that do not admit closed-form valuation formulas, this poses a significant computational challenge. This problem is addressed by proposing a static replication algorithm for interest rate options with early-exercise features under an affine term-structure model. Under the appropriate conditions, we can find an equivalent portfolio of vanilla options that replicate these products. Specifically, we decompose the product into a portfolio of European swaptions. The weights and strikes of the portfolio are obtained by regressing the target option value with interpretable, feed-forward neural networks. Once an equivalent portfolio of European swaptions is determined, we can leverage on closed-form expressions to obtain the conditional prices and sensitivities, which serve as an input to exposure and SIMM-driven MVA quantification. For a consistent forward sensitivity estimation, this involves the differentiation of the portfolio-weights. The accuracy and convergence of the method is demonstrated through several representative numerical examples, benchmarked against the established least-square Monte Carlo method.

Recent Development in Credit Risk Measurement and Credit Risk Modelling with Respect to Different Types of Borrowers

Recent Development in Credit Risk Measurement and Credit Risk Modelling with Respect to Different Types of Borrowers

Credit Risk Measurement Using Score-Driven Clustering with An Application to Bank Customers in Guatemala

Credit Risk Measurement Using Score-Driven Clustering with An Application to Bank Customers in Guatemala

Research on the Financial Credit Risk Management Model of Real Estate Supply Chain Based on GA-SVM Algorithm: A Comprehensive Evaluation of AI Model and Traditional Model

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value.

我国上市商业银行信用风险度量研究——基于KMV模型

我国上市商业银行信用风险度量研究——基于KMV模型

How does the fed affect corporate credit costs? Default risk, creditor segmentation and the post-FOMC drift

Surprise changes in monetary policy rates have a causal impact on credit risk measures, which display a significant post-FOMC drift. I employ a tight identification strategy to decompose the influence of firm-specific and creditor-specific factors across horizons. Firms with narrower income gaps and lower Tobin’s Q ratios exhibit heightened sensitivity at both short and long horizons. Bonds predominantly held by bond funds demonstrate only temporarily more sensitivity, indicating that credit market segmentation fails to account for the observed drift. Aggregate broker dealer capital scarcity is linked to an amplified response in the drift component. A large portion of the drift remains unexplained, revealing the limitations of cross-sectional characteristics in explaining the transmission mechanism.

Analisis Penerapan Manajemen Risiko Kredit pada Perusahaan Start-Up Fintech PT A

Fintech PT A is one of the pioneers in Indonesia that serves business people with lenders on a digital platform. One of the problems faced by fintech PT A is the increase in non-performing loans. This research is a case study at a fintech startup to analyze the application of credit risk management and provide recommendations for improving credit risk management to improve company performance. The methodology used in this research is descriptive qualitative analysis by collecting data from interviews and documentation studies. Interviews were conducted with the Director of Credit and Collection and Director of Risk who have a crucial role in risk management in the company. The results of the study show that the application of risk management to PT A is in accordance with the four stages of the Indonesia’s Financial Services Authority regulation number 04/POJK.05/2021 namely credit risk identification, credit risk measurement, credit risk control, and credit risk monitoring. However, the application of credit risk management by PT A based on data analysis obtained through interviews and documentation studies shows that the results have not operate optimally. Because at the stages of monitoring credit risk and controlling credit risk there are still several aspects that have not been fulfilled based on the Indonesia’s Financial Services Authority regulation number 04/POJK.05/2021. So the recommendation that can be given is that the company can create a more structured and sustainable risk management system with all related divisions so that it can accommodate all aspects of risk up to the risk monitoring and control process.

Credit Risk Research on Chinese Real Estate Enterprises Based on Modified KMV Model

This paper first analyses the current situation of credit risk in China’s real estate industry, and then compares the traditional and modern credit risk measurement models. On this basis, the KMV model is selected, and the artificial intelligence model Genetic Algorithm (GA) and GARCH model are introduced to improve the accuracy of the KMV model. Secondly, the annual financial data and stock trading data of 24 real estate listed companies for 2018 – 2022 are selected for empirical research. By analyzing the total default distance of the 24 companies and the actual economic development of China, it is proved that the results of the GA-GARCH-KMV model are 8% more correct than the classical KMV model, which indicates that the model has better applicability.

Analysis of Collateral Risk Mitigation Without Collateral Rights at PT. Permodalan Nasional Madani Branch Office Jakarta

This study aims to analyze the implementation of strategic risk management activities for non-mortgage guarantees, analyze risk management that occurs if a guarantee is not installed with mortgage rights in it and analyze the executor's efforts if there is bad or problematic financing at PT. Permodalan Nasional Madani Branch Office Jakarta. Researchers used a type of field research (field research), with a qualitative approach, using data obtained directly from employees PT. Permodalan Nasional Madani Branch Office Jakarta. Data was collected by means of interviews, observation and documentation. Data validity analysis used source triangulation, data analysis used data reduction, data presentation and conclusion. Identification and measurement of non-mortgage credit risk is carried out by PNM before extending credit to prospective customers. PNM conducts assessments, surveys of customer backgrounds, businesses and guarantees provided by customers. Monitoring and controlling credit risk is carried out when credit has been given to customers. Steps taken include persuasive billing and visits to troubled customers.

EFFECT OF CREDIT RISK MANAGEMENT ON THE FINANCIAL PERFORMANCE OF BANKING SECTOR OF BANGLADESH: A STUDY ON GENERATION-BASED SELECTED LISTED COMMERCIAL BANKS

<p>The financial sector of the country is mostly comprised of banking institutions; those are leading the economy with great exposure with the contribution to the development process. However, the banking sector of Bangladesh is disturbed by the large-scale amount of non-performing loans while has become an essential part of the finance of the industries. As part of the measurement of credit risk and macro factors average lending rate, inflation, NPL size, capital adequacy ratio, liquidity ratio have been selected to test the influence on the financial performance found through the return on asset of the selected banks. To conduct the study 9 banks of three generations have been selected for the period of 2016 to 2022. Robust least square method of regression and error correction term have been run to oversee the real impact on financial performance while endogenity and random walk in the values are being considered to overcome through a dynamic regression model. NPL has a negative impact on the performance and average lending rate, inflation, liquidity ratio and capital adequacy ratio bring a positive impact on the financial performance of the banks. Breusch-Pagan LM test confirms that cross-sectional dependency exists and VAR serial correlation test finds autocorrelation in the data set. The policy implication of this study suggests that the high NPL ratio must be reduced and CAR and LR must be improved to get the desired results of the performance. Strong fiscal and banking regulation should implement so that governance can be ensured to create responsibility and financial strength.</p><p><strong>JEL:</strong> E4, D81, E33, E44</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0045/a.php" alt="Hit counter" /></p>

Investigating the relationship between monetary policy, macro-prudential policy and credit risk in Indonesia banking industry

Using a novel panel data set we study the influence of monetary and macro-prudential policies on non-performing loans as a measure of credit risk in Indonesian banking industry from Q1 2010 to Q4 2022. The panel homogeneity assumption was verified through the utilization of the Chow and Roy-Zellner tests. The findings showed that the model was not homogenous, necessitating the use of the Pooled Mean Group (PMG) estimator. The results indicated that monetary and macro-prudential policies significantly impacted credit risk. Furthermore, tight monetary and macro-prudential policies increased and reduced credit risk in the long run, respectively. The findings also showed that a loosening monetary policy reduced credit risk in the short run. Therefore, higher authorities must establish effective monetary and macro-prudential policies to reduce the non-performing loan ratio and maintain credit risk in Indonesia's banking industry.

Can market information outperform hard and soft information in predicting corporate defaults?

AbstractRecent evidence has shown that hybrid models for credit ratings are important when assessing the risk of firms. Within this stream of literature, we aim to provide novel evidence on how hard (quantitative), soft (qualitative), and market information predict corporate defaults for unlisted firms by implementing the Cox proportional hazard model. We address this research question by exploiting a unique proprietary dataset comprising of detailed information on internal credit ratings of European unlisted mid‐sized firms and compute their Merton's distance‐to‐default (DD) measure of credit risk with market data collected on comparable publicly listed companies. Our results show that the bank's use of hard, soft, and market information when assessing the credit ratings of borrowers has a significant influence on the prediction of their defaults. Further, we investigate the significant influence of soft information in predicting corporate defaults by drawing on two separate processes through which loan officers can inject soft information in credit scoring, that is, ‘codified’ and ‘uncodified’ discretion. Finally, when we distinguish between the loan officer's discretion to upgrade or downgrade an applicant's credit score, we find that it is the upgrade that is likely to predict a lower probability of a firm defaulting. This study contributes to the policy debate on safeguarding the banking sector's continuity by positing that integrating market information into banks' hybrid methods of credit rating helps to improve the accuracy in predicting unlisted firms' credit risk that is useful to policy makers for the design of future forward‐looking financial risk management frameworks.