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Journal articles on the topic 'KMV Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Yang, Liu, and Fang Qiquan. "Credit Risk Research on Chinese Real Estate Enterprises Based on Modified KMV Model." International Journal of Current Science Research and Review 06, no. 09 (2023): 6229–35. https://doi.org/10.5281/zenodo.8336988.

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Abstract : 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.

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2

Ruijie, Liu, and Zhang Xinyan. "Credit Risk Measure of Listed Pharmaceutical and Biological Companies Based on Genetic Algorithm KMV Model." International Journal of Engineering Research & Science 9, no. 4 (2023): 01–09. https://doi.org/10.5281/zenodo.7879808.

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<strong>Abstract</strong><strong>—</strong> In order to measure the credit risk of listed companies in China's pharmaceutical and biological industry, a total of 28 listed companies in the A-share ST category and non-ST category were therefore selected as samples, and the model was improved using genetic algorithms, while the credit risk of 290 listed companies in the A-share pharmaceutical and biological industry in 2019-2021 was analyzed based on the improved model, and the research results showed that: the improved KMV model can effectively identify the improved KMV model can effectively identify the credit risk of listed companies in the industry, and the accuracy of the improved KMV model in determining whether an enterprise is in default reaches 78.57%; the credit risk of the pharmaceutical and biological industry decreases in the year of the outbreak of the new crown epidemic in 2020, and increases and the credit risk of enterprises appears polarized one year after the outbreak of the epidemic.

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3

Tasken, Senbati, and Haibo Yan. "Study of Green Credit Risk in the Steel Industry Considering Exogenous Shocks." Academic Journal of Management and Social Sciences 7, no. 2 (2024): 38–43. http://dx.doi.org/10.54097/cxzn9s85.

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Based on the ESG-KMV model constructed by introducing industry ESG thresholds and ESG score values into the traditional KMV model's enterprise asset value and enterprise default point, a nonlinear mathematical expectation ESG-KMV model was constructed from the perspective of a commercial bank considering exogenous shocks and other factors, in order to measure the impact of exogenous shocks on the green credit risk of iron and steel enterprises. Results: The ESG-KMV model based on nonlinear expectation modification is introduced under the consideration of exogenous shocks, and the measurement results show that the default distance in the control group is relatively stable, while the default distance in the default group becomes sharply larger and smaller, and then becomes stable and smaller, which indicates that the model can effectively measure the impact of exogenous shocks on the green credit risk of iron and steel enterprises.

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4

Tarsicius Sunaryo. "Mengukur Risiko Kredit dengan Model Merton." JURNAL MANAJEMEN RISIKO 3, no. 1 (2022): 29–41. http://dx.doi.org/10.33541/mr.v3i1.4546.

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Abstract: Nilai perusahaan sama dengan penjumlahan nilai saham dan nilai utang (bond atau kewajiban) perusahaan. Nilai perusahaan berfluktuasi. Bila nilai perusahaan lebih kecil dibanding nilai bond perusahaan, maka perusahaan default. KMV menentukan bahwa titik default perusahaan sama dengan nilai utang jangka pendek dan setengah dari utang jangka panjangnya. Semakin tinggi nilai perusahaan, semakin kecil perusahaan default. KMV memetakan jarak dari nilai perusahaan ke titik default ke frekuensi default (expected default frequency). Keywords: risk/credit sensitive bond,leverage, probaility of default, default point, distane to default, mapping, expeced default frequencies, credit loss, expected credit loss, credit put, credit derivative.

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5

Chen, Haojie, Ng Sin Huei, and Lew Shian Loong. "Application of Credit Risk Management Model in Chinese Banks." Asian Journal of Finance & Accounting 11, no. 1 (2019): 141. http://dx.doi.org/10.5296/ajfa.v11i1.14168.

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The main objective of this paper is to perform empirical analysis and research on the KMV and Zeta models, discussing whether banks in China could adopt both models in their credit risk management practices. In order to measure credit risk, the KMV model focuses on “Expected Default Probability” (EDP) that is calculated using Black-Scholes Option Pricing Formula. On the other hand, the Zeta Model focuses on determining the probability of a company going bankrupt two years prior to the event. Previous research on risk management has shown that the primary risk the banks generally face is credit risk as an increasingly greater number of banks suffer losses because of credit issues. This paper therefore aims to add to the existing literature a strong case for the relevance of both the KMV and Zeta models to be considered in the topic of banks’ credit risk management.

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Yusof, Norliza Muhamad, Iman Qamalia Alias, Ainee Jahirah Md Kassim, and Farah Liyana Natasha Mohd Zaidi. "Determining the Credit Score and Credit Rating of Firms using the Combination of KMV-Merton Model and Financial Ratios." Science and Technology Indonesia 6, no. 3 (2021): 105–12. http://dx.doi.org/10.26554/sti.2021.6.3.105-112.

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Credit risk management has become a must in this era due to the increase in the number of businesses defaulting. Building upon the legacy of Kealhofer, McQuown, and Vasicek (KMV), a mathematical model is introduced based on Merton model called KMV-Merton model to predict the credit risk of firms. The KMV-Merton model is commonly used in previous default studies but is said to be lacking in necessary detail. Hence, this study aims to combine the KMV-Merton model with the financial ratios to determine the firms’ credit scores and ratings. Based on the sample data of four firms, the KMV-Merton model is used to estimate the default probabilities. The data is also used to estimate the firms’ liquidity, solvency, indebtedness, return on asset (ROA), and interest coverage. According to the weightages established in this analysis, scores were assigned based on those estimates to calculate the total credit score. The firms were then given a rating based on their respective credit score. The credit ratings are compared to the real credit ratings rated by Malaysian Rating Corporation Berhad (MARC). According to the comparison, three of the four companies have credit scores that are comparable to MARC’s. Two A-rated firms and one D-rated firm have the same ratings. The other receives a C instead of a B. This shows that the credit scoring technique used can grade the low and the high credit risk firms, but not strictly for a firm with a medium level of credit risk. Although research on credit scoring have been done previously, the combination of KMV-Merton model and financial ratios in one credit scoring model based on the calculated weightages gives new branch to the current studies. In practice, this study aids risk managers, bankers, and investors in making wise decisions through a smooth and persuasive process of monitoring firms’ credit risk.

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7

Septiana, Nurul Izzati, Fatin Salwa Binti Haji Katri, Azlina Binti Ideris, and Nadiana Noryatim. "Estimating Expected Default Probability and Credit Risk Spread Using the KMV Merton Model: A Case Study of Bank Islam Malaysia Berhad." Jihbiz : Jurnal Ekonomi, Keuangan dan Perbankan Syariah 9, no. 1 (2025): 93–105. https://doi.org/10.33379/jihbiz.v9i1.6373.

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The objective of the paper is to estimate the expected default probability and credit risk spread of Bank Islam Malaysia using the KMV Merton Model and analyse the strengths of the KMV model. The data used in this research are short term and long-term liabilities data of Bank Islam Malaysia Berhad that we obtained from the financial statements, stock prices and number of stocks traded of BIMB and Islamic Bank-relevant interest rates data that we obtained from the Overnight Policy Rate Decision (OPR) by Bank Negara Malaysia. The data is then prepared and interpolated to match weekly period before applying the KMV Model using Microsoft Excel to calculate the default probability and credit risk spread of BIMB. The result shows that the Probability of Default for BIMB is nearly 0.0001 for the first 3 years of its debt maturity period and nearly 0.0007 and 0.002 for the maturity period of 4 and 5 years respectively. Credit risk spread for BIMB is nearly null for the first 2 years maturity period then gradually increases each year to 0.01, 0.05 and 0.14. BIMB’s distance to default ranges between 9.17-10.01. The research indicates that the BIMB’s calculated PD is reliable and that the KMV Merton Model provides high accuracy and reliability of credit risk measurements. The KMV Model can be applied to different types of companies including Islamic Bank.

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8

Zhang, Qingyuan. "Research on Bank Financial Risk Control Mechanism Based on KMV Model." Frontiers in Business, Economics and Management 6, no. 3 (2022): 241–44. http://dx.doi.org/10.54097/fbem.v6i3.3628.

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Starting from the loan users, KMV model transfers the evaluation of credit risk from the perspective of banks to the perspective of repayment enterprises, and judges whether the lending enterprises have repayment ability as the basis for evaluating credit risk. Credit risk is the core risk faced by commercial banks. When the economic situation fluctuates, people's economic expectations will gradually change, and the behavior of borrowers will also change. This paper studies the financial risk control mechanism of banks based on KMV model. This paper studies the credit risk status of 11 listed banks in China from 2018 to 2021, in order to observe whether macroeconomic changes have affected them since the new normal of economy. The empirical results show that the volatility of large commercial banks in each year is obviously lower than that of small and medium-sized commercial banks. The fluctuation range of weighted average default distance of large commercial banks is obviously smaller than that of small and medium-sized commercial banks. The empirical results show that KMV model has strong credit risk identification ability. The smaller the average default distance of an enterprise, the greater the corresponding default risk.

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9

Zhang, H., Y. W. Guo, Y. Hou, L. Tang, and M. Deveci. "Improved Whale Optimization Algorithm for supply chain financial risk assessment of cloud warehouse platform." Advances in Production Engineering & Management 19, no. 3 (2024): 395–407. https://doi.org/10.14743/apem2024.3.515.

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This study provides an in-depth analysis of a new financial model for cloud warehouses and evaluates the associated credit risk within the context of supply chain financing, focusing on the intelligent transformation in this field. Concurrently, an optimization problem was derived from the evaluation issue, with the whale optimization algorithm (WOA) used to identify a reasonable default point and distance. To simplify the identification of these points, we enhanced the traditional WOA, resulting in an improved version, the IWOA, which demonstrated very good optimization performance. The IWOA's optimization capabilities were applied to determine the optimal ratio of short- and long-term debt coefficients, identifying the default point in the Kealhofer, McQuown, and Vasicek (KMV) credit monitoring model, replacing fixed values and yielding more precise results. Furthermore, this study introduces a novel analytical approach to credit risk measurement, advancing the development of related theories and methods. Accurate analysis of financial stability and risk is crucial in industrial sectors, including engineering and manufacturing. The simulation using specific data revealed that the IWOA-KMV model exhibited better and faster optimization capabilities, with greater discrimination ability compared to the KMV model. Overall, this study examines the risk factors in the cloud warehouse financing model, offers an improved version of the WOA, introduces a modified IWOA-KMV model to create a scientific, practical credit risk assessment framework, and provides guidance for risk control in cloud warehouse financing, a novel financing service.

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10

Gurny, Martin, Sergio Ortobelli Lozza, and Rosella Giacometti. "Structural Credit Risk Models with Subordinated Processes." Journal of Applied Mathematics 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/138272.

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We discuss structural models based on Merton's framework. First, we observe that the classical assumptions of the Merton model are generally rejected. Secondly, we implement a structural credit risk model based on stable non-Gaussian processes as a representative of subordinated models in order to overcome some drawbacks of the Merton one. Finally, following the KMV-Merton estimation methodology, we propose an empirical comparison between the results obtained from the classical KMV-Merton model and the stable Paretian one. In particular, we suggest alternative parameter estimation for subordinated processes, and we optimize the performance for the stable Paretian model.

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11

Zhan, Ni, Liang Lin, and Ting Lou. "Research on Credit Risk Measurement Based on Uncertain KMV Model." Journal of Applied Mathematics and Physics 01, no. 05 (2013): 12–17. http://dx.doi.org/10.4236/jamp.2013.15003.

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12

Liu, Chao, Xiaofan Zhang, and Yuerong Wang. "Research on Optimal Allocation Strategy of Bank Credit Funds Based on KMV Model and Logit Model." Finance and Market 6, no. 1 (2021): 1. http://dx.doi.org/10.18686/fm.v6i1.3061.

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Using KMV model, normal Copula function, K-means cluster analysis and logit model, this paper constructs the enterprise credit risk assessment model, bank credit fund optimal allocation model, banking risk index system, and synthetically uses software such as MATLAB、SPSS to solve the problem of credit fund distribution strategy for small and medium-sized enterprises, and draws the conclusion that the loan interest rate classification of enterprise credit risk assessment, the weight of bank to credit fund distribution, and the change of bank risk index weight in sudden situation.Finally, the above model provides the strategy for bank credit fund allocation and gives the test and evaluation. The outstanding features of this paper are: using the KMV model and the normal Copula function, combining the enterprise credit rating and default times to establish a linear model to quantify the enterprise credit risk, will not beeasy to calculate the industry violation probability quantitative analysis, also get the bank credit annual interest rate fordifferent industries and levels of enterprises, and through the representative industries of the optimal loan weight calculation, so that the bank decision has the characteristics of the least unit risk. This paper also establishes a banking risk index system with emergency factors, which is of practical significance to make decision analysis of emergency events.

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13

Shen, LiJun, and Yu He. "COVID-19’s influence on the credit risks and enterprise innovation of the Guangxi manufacturing——Based on the measure of KMV model." E3S Web of Conferences 275 (2021): 03071. http://dx.doi.org/10.1051/e3sconf/202127503071.

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The paper used the KMV model to manufacturing industry of Guangxi in China to concretely abstract the credit risk and enterprise innovation into a measurable quantitative index, and compare the changes in credit risk before and after COVID-19. This paper selects 17 Listed Companies in Guangxi manufacturing industry as empirical samples, and calculates the expected default rate of different companies by using the traditional and modified KMV models. The larger the index value is, the higher the credit risk is, And then affect the enterprise innovation activities. The results show that the overall credit risk management ability of Guangxi’s manufacturing industry is relatively high, but by the impact of COVID-19, credit risk has increased. If left unguarded, it will have an impact on enterprise innovation.

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14

Gottschalk, Sylvia. "Asset correlation, portfolio diversification and regulatory capital in the Basel Capital Accord." Risk Governance and Control: Financial Markets and Institutions 1, no. 3 (2011): 31–39. http://dx.doi.org/10.22495/rgcv1i3art3.

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In this paper, we analyze the properties of the KMV model of credit portfolio loss. This theoretical model constitutes the cornerstone of Basel II’s Internal Ratings Based(IRB) approach to regulatory capital. Our results show that this model tends to overestimate the probability of portfolio loss when the probability of default of a single firm and the firms’ asset correlations are low. On the contrary, probabilities of portfolio loss are underestimated when the probability of default of a single firm and asset correlations are high. Moreover, the relationship between asset correlation and probability of loan portfolio loss is only consistent at very high quantiles of the portfolio loss distribution. These are precisely those adopted by the Basel II Capital Accord for the calculations of capital adequacy provisions. So, although the counterintuitive properties of the KMV model do not extend to Basel II, they do restrict its generality as a model of credit portfolio loss.

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ZHANG Yaqiong. "The Empirical Research on Chinese Stock Market Based on KMV Model." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 10 (2013): 522–30. http://dx.doi.org/10.4156/aiss.vol5.issue10.61.

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16

Yang, Yang, Li Li, Zongfang Zhou, and Wenying Fei. "The Research on Applicability of Amended KMV Model with Different Industries." Journal of Risk Analysis and Crisis Response 3, no. 1 (2013): 27. http://dx.doi.org/10.2991/jrarc.2013.3.1.4.

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17

吴, 常玉. "Research on Credit Risk of Listed SMEs Based on KMV Model." E-Commerce Letters 13, no. 04 (2024): 2639–49. http://dx.doi.org/10.12677/ecl.2024.1341439.

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18

Shen, Junchao, and Jiayan Chen. "Application of KMV Model in Credit Risk Management in Banking Industry." Journal of Theory and Practice of Management Science 4, no. 05 (2024): 1–12. http://dx.doi.org/10.53469/jtpms.2024.04(05).01.

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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.

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19

Gupta, Vandana. "An Empirical Analysis of Default Prediction Models: Evidence from lndian Listed Companies." Journal of Prediction Markets 8, no. 3 (2015): 1–23. http://dx.doi.org/10.5750/jpm.v8i3.946.

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This paper attempts to evaluate the predictive ability of three default prediction models: the market-based KMV model, the Z-score model using discriminant analysis (DA), and the logit model; and identifies the key default drivers. The research extends prior empirical work by modeling and testing the impact of financial ratios, macro-economic factors, corporate governance and firm-specific variables in predicting default. For the market-based model, the author has extended the works of KMV in developing a suitable algorithm for determining probability of default (PD). While for the KMV model, the continuous observations of PD are used as the dependent variable, for the accounting-based models, ratings assigned are the proxy for default (those rated ’D’ are defaulted and rated ‘AAA’ and ‘A’ are solvent). The research findings largely support the hypothesis that solvency, profitability and liquidity ratios do impact the default risk, but adding other covariates improves the predictive ability of the models. Through this study, the author recommends that accounting –based models and market based models are conceptually different. While market-based models are forward looking and inclusion of market data makes the default risk quantifiable; to make the PD more exhaustive, it is important to factor in the information provided in the financial statements. The conclusions drawn are that the disclosures in financial statements can help predict default risk as financial distress risk is likely to evolve over time and will be reflected in financial statements beyond accounting ratios. Moreover this will also help divulge “creative accounting” practices by corporates.

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20

Gao, Zixuan. "Research on Default Risk of Urban Investment Bonds Based on Multi-factor Model and KMV Model." Frontiers in Business, Economics and Management 17, no. 2 (2024): 450–54. https://doi.org/10.54097/py9e9n85.

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With the acceleration of urbanization in China, the urban investment bond market is facing increasing risks and challenges while providing financial support for infrastructure construction. Aiming at the complexity and uncertainty of the market, the study aims to improve the accuracy and applicability of risk measurement through model fusion. This study will comprehensively consider market factors, company financial situation and macroeconomic indicators, and use the comprehensiveness of multi-factor model and the accurate estimation of default probability by KMV model. The results show that the combination of data collection, model construction, factor optimization and model validation can provide investors and regulators with scientific decision-making and effective supervision. Promote the stable development of the market.

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21

Deng, Lingfeng, Wei Li, Juan Liu, and Yizhen Ouyang. "Credit risk of China’s commercial banks based on the KMV model—taking 18 listed commercial banks as an example." Journal of Computational Methods in Sciences and Engineering 24, no. 6 (2024): 3446–54. https://doi.org/10.1177/14727978241292905.

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Credit risk constitutes a pivotal concern within the purview of commercial banking operations, commanding profound significance for regulators, the general public, and investors alike. In pursuit of a comprehensive comprehension of the intricacies of credit risk within the banking sector, this study selects a diverse cohort of 18 publicly traded commercial banks in China as its focal subjects. Employing financial and stock data from the year 2019, this research leverages the advanced KMV model to evaluate these banks’ default risks and subsequently calculate their respective default distances. The innovation of this paper lies in that we will use KMV model to quantify the credit risk level of commercial banks, and empirically study the credit risk of Chinese commercial banks through the case study of 18 listed commercial banks with different nature. The KMV model, a well-established approach for the assessment of default risk, computes the default distance predicated upon the market value’s volatility and the balance sheet’s structure, encompassing considerations of the debtor’s probability of default and default loss rate. Default distance denotes the discernible disparity between the debtor’s prevailing market value and the precipice of default, serving as a reliable metric to gauge the probability of a debtor’s default occurrence. This study meticulously curates financial and stock data pertaining to the 18 selected banks, subsequently subjecting them to rigorous evaluation through the KMV model. Through a meticulous analysis of the default distances thus derived, this research unveils the divergent spectra of default risks across banks of varying profiles, further elucidating potential risk factors. These analysis results are of important reference value for regulators and investors. Regulators can identify the potential high-risk institutions more accurately by analyzing the default distance of banks, so as to strengthen targeted regulatory measures and ensure the sound operation of the entire banking industry. At the same time, investors can also refer to these analysis results when making investment decisions to understand the credit status and risk levels of different banks, so as to better diversify risks and optimize the investment portfolio. In short, through the analysis of the default distance of banks, we can have a deeper understanding of the differences and potential risk factors in the default risk of banks of different properties, so as to provide strong decision support for regulators and investors.

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Zhuo, Zirong, Jixiang Liu, and Wenmin Luo. "Credit Default Risk Assessment of Local Government Debts Based on KMV Model." International Journal of Economics and Finance 8, no. 5 (2016): 230. http://dx.doi.org/10.5539/ijef.v8n5p230.

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With the continuing expansion of Chinese local government debts, its credit risk issues raise the public attention. According to the overall statistics data in Chinese Statistic Bureau, there’re various scales of debts exist, undertaken by Chinese prefecture-level cities’ local government. Some of them exceed the alerting level of international line. In an effort to measure the credit default risk level of Chinese local governments, this paper makes a moderate assessment of credit default risk based on modified KMV model. In conditions of a variety of local government revenue, this model calculates the distance from default and default possibility of local government debts under different guarantee proportion. Meanwhile, this paper also explores the variation of local governments’ credit default risk when they use different financial ratio of financing for the construction of urban infrastructure. Finally, we reach the conclusion that the expected default probability shrinks as guarantee proportion raises, and increases as financing proportion raises; under a 40% of guarantee proportion, expected default rates are low with controllable risks; And within a financing proportion of 50%, chances of default as well as risks, are low.

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Bingzheng, Yan, and Bai Puxian. "Research on Credit Risk Assessment of Commercial Banks Based on KMV Model." Social Sciences 10, no. 5 (2021): 204. http://dx.doi.org/10.11648/j.ss.20211005.11.

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Wu, Hsu-Che, and Yu-Ting Wu. "Evaluating credit rating prediction by using the KMV model and random forest." Kybernetes 45, no. 10 (2016): 1637–51. http://dx.doi.org/10.1108/k-12-2014-0285.

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Purpose An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues. Design/methodology/approach A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models. Findings The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity. Research limitations/implications The experimental cases were based on financial data from public companies in North America. Practical implications Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption. Originality/value The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.

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Raj, Kumar Sudheer, and Mahadev Ota. "Modelling credit risk using Merton-KMV model: evidence from selected Indian firms." International Journal of Business Continuity and Risk Management 14, no. 2 (2024): 119–38. http://dx.doi.org/10.1504/ijbcrm.2024.139035.

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丁, 珊珊. "Research on Credit Risk Measurement of Trust Companies Based on KMV Model." E-Commerce Letters 14, no. 05 (2025): 868–76. https://doi.org/10.12677/ecl.2025.1451356.

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27

Anggoro, Agil Setyo, Mustafid Mustafid, and Puspita Kartikasari. "PENDEKATAN MODEL KMV MERTON UNTUK PENGUKURAN NILAI RISIKO KREDIT OBLIGASI EXPECTED DEFAULT FREQUENCY (EDF) DILENGKAPI GUI R." Jurnal Gaussian 12, no. 1 (2022): 92–103. http://dx.doi.org/10.14710/j.gauss.12.1.92-103.

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Bonds are debt securities from the issuer to bondholders with a promise to pay off the principal and the coupon at maturity. Bond investing can generate income while also posing investment risks. One of the risks connected with bond investing is credit risk, which might manifest as a firm collapsing (default). The KMV Merton model approach is one method of measuring bond credit risk. This Merton KMV model computes the Expected Default Frequency (EDF), which is the likelihood of a firm failing in the following years or years. The data processing system using the Graphical User Interface (GUI) can facilitate the analysis process by implementing the Shiny Package in the R studio program. This research case makes use of up to 48 months of monthly corporate asset data from January 2018 to December 2021. The results obtained the value of Expected Default Frequency (EDF) in each company, namely PT Bank Mandiri Tbk obtained a value of 0% and PT Bank Rakyat Indonesia Tbk obtained a value of 1,406668E-113%. Because PT Bank Rakyat Indonesia Tbk's percentage return is higher than that of PT Bank Mandiri Tbk, investors would be better off investing in bonds at PT Bank Mandiri Tbk.

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Zhu, Shenghua. "Risk Assessment of Local Government Debt based on KMV Model: A Case Study of Hangzhou, Zhejiang Province." Scientific and Social Research 3, no. 6 (2021): 175–81. http://dx.doi.org/10.36922/ssr.v3i6.1297.

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The present government debt governance focuses on calculating, preventing, and controlling local government debt risk. The default probability of local government debt in Hangzhou, Zhejiang Province, is calculated using a modified “Kealhofer, McQuown, and Vasicek” (KMV) model. The findings reveal that Hangzhou’s debt risk in the next three years is usually manageable, but that debt risk will progressively emerge in the coming years when the debt payback cycle begins.

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29

Zeng, Zhisen, Huixian Zeng, and Siqi Jiang. "The application of KMV model in China’s insurance market during the COVID-19." IOP Conference Series: Earth and Environmental Science 692, no. 3 (2021): 032032. http://dx.doi.org/10.1088/1755-1315/692/3/032032.

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Xu, Zhiheng, Wen Fan, and Fan Zhu. "Research on Regional Debt Risk in Hubei Province Based on Modified KMV Model." IOP Conference Series: Materials Science and Engineering 768 (March 31, 2020): 052129. http://dx.doi.org/10.1088/1757-899x/768/5/052129.

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Sun, Jifeng, and Tingwei Sun. "Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market." Journal of Finance Research 4, no. 1 (2020): 59. http://dx.doi.org/10.26549/jfr.v4i1.3483.

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In recent years, China's bond market has experienced rapid development, but the pace of credit risk supervision has not kept up. Since 2014, the number of domestic credit bond defaults has increased. In 2016, there were 79 domestic default bonds, with a default amount of up to 40.3 billion Yuan. From the perspective of domestic bond market credit risk supervision and early warning mechanism, rating is not objective, and tracking is not timely also rating methods are backward. Therefore, with the development of big data and other technologies, it is urgent to study credit risk supervision methods suitable for the domestic bond market. On the basis of combing the development of domestic bond market and analyzing the current situation of domestic credit rating, this paper combines the results of theoretical research at home and abroad, the information available in the domestic market, big data mining and automation technology, based on the financial and stock exchange information of listed companies, combined with BS option pricing theory, constructs KMV model.

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李, 小峰. "Measurement of Financial Default Risk of Automobile Supply Chain Based on KMV Model." Finance 14, no. 04 (2024): 1456–66. http://dx.doi.org/10.12677/fin.2024.144150.

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梁, 凯豪. "Measurement and Empirical Analysis of Listed Companies Debt Default Probability via KMV Model." Statistics and Application 08, no. 01 (2019): 6–17. http://dx.doi.org/10.12677/sa.2019.81002.

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Lin, Liang, Ting Lou, and Ni Zhan. "Empirical Study on Credit Risk of Our Listed Company Based on KMV Model." Applied Mathematics 05, no. 13 (2014): 2098–106. http://dx.doi.org/10.4236/am.2014.513204.

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Wei, Chengtai, Mingyang Shen, Zizhao Zheng, Wenzheng Meng, and Zejiong Zhou. "Exploration and Improvement Path of Local Government Debt Risk Warning Mechanism Based on KMV Model: Taking Bengbu, Anhui as an Example." International Journal of Global Economics and Management 1, no. 1 (2024): 123–35. http://dx.doi.org/10.62051/ijgem.v1n1.18.

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This article is based on the KMV model and explores methods for improving the local government debt risk warning mechanism under the backdrop of the pandemic, with a case study of Bengbu City in Anhui Province, China. In the context of an economic downturn and the impact of the COVID-19 pandemic, accurately understanding and effectively managing the fiscal risks of local governments is particularly important. The study focuses on analyzing the impact of changes in fiscal revenue and expenditure structures during the pandemic on local debt risks, using the KMV model for in-depth analysis. By comparing fiscal data from different time periods, the effectiveness of the existing local government debt risk warning mechanisms was evaluated. This paper also discusses how to improve risk management and proposes specific strategies and suggestions. These recommendations aim to enhance the efficacy of local government debt warning systems, especially considering the unique challenges brought about by the pandemic and economic changes. The study shows that by improving fiscal risk management and optimizing warning mechanisms, it is possible to better respond to various economic challenges that may arise in the future, providing local governments with more robust and reliable financial risk control plans.

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Xu, Kangming, and Biswajit Purkayastha. "Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment." Academic Journal of Sociology and Management 2, no. 6 (2024): 19–24. https://doi.org/10.5281/zenodo.14077150.

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With the continuous development of artificial intelligence and various new intelligent algorithm technologies, the business contacts between various institutions within financial enterprises are gradually increasing, and traditional financial risk management can no longer adapt to the current status quo in the era of big data. The lack of information sharing among institutions can reduce the efficiency of financial management and adversely affect the operation of enterprises. At present, financial credit risk mainly includes credit risk, market risk and operational risk. Credit risk relates to the possibility that a borrower will not be able to repay loans or debts on time, market risk covers potential losses caused by market volatility, price changes and adverse events, while operational risk includes risks such as internal operational errors, technical failures and fraud, which may adversely affect the normal operations and financial condition of a financial institution. These risk factors need to be integrated and managed in the financial sector to ensure financial stability and customer trust. Therefore, this paper aims to establish a KMV financial credit risk model, continuously strengthen the internal risk management of enterprises, achieve management modeling and a good KMV algorithm mechanism, and realize the cooperation and stickiness between customers and enterprises, so as to avoid unnecessary financial risks.

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Liu, Cheng-yong, Tian-yu Dong, and Ling-xing Meng. "The Prevention of Financial Legal Risks of B2B E-commerce Supply Chain." Wireless Communications and Mobile Computing 2022 (January 21, 2022): 1–15. http://dx.doi.org/10.1155/2022/6154011.

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B2B supply chain finance is a new type of financial model, which is created to help companies raise funds and promote the production, operation, and development of companies in the supply chain. Based on the B2B e-commerce platform, it is used for online transactions and transactions between companies and companies. Information, integrating logistics, business flow, information flow, and capital flow for data analysis and processing. When people enjoy convenient and fast e-shopping, they must not only choose products carefully but also understand and be familiar with the relevant laws and regulations of shopping on the Internet. Avoiding potential legal risks is a key factor. The purpose of this article is to analyze the financial risks of the B2B e-commerce supply chain, according to the current Internet development trend, study the legal risks of the B2B e-commerce supply chain in the development, put forward corresponding recommendations, and build a relevant system to reduce risks. Combining some current legal issues faced by e-commerce, this article first analyzes the generation and operation mechanism of credit risk under the B2B platform online supply chain financial business model; then, based on the supply chain financial risk, a relevant system is constructed to reduce risks. This article first analyzes the generation and operation mechanism of credit risk under the online supply chain financial business model of the B2B platform; then, based on the supply chain financial risks, construct a system that can prevent and control the risks generated under this financial business model risk evaluation index system; finally, the KMV model and case are analyzed to verify whether this risk evaluation research is effective for supply chain financial risks. The experimental results show that through the KMV model, comparing the two sets of data, the default distance of most default groups is smaller than that of the normal group. It can be seen that the greater the default distance, the smaller the credit risk. When the default point coefficient is 0.85, use the KMV model which is most obvious when judging the company’s overall probability of default.

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Mansoor, Karrar Jabbar, and Sadeq Abdulridha Gatea Kaabi. "Topical Polyethylene Glycol-Phage Ointment as a Therapy to Treat Burn-Wound Infection Using Mice Model." Al-Mustansiriyah Journal of Science 35, no. 2 (2024): 83–95. http://dx.doi.org/10.23851/mjs.v35i2.1477.

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Background: One of the most significant problems facing public health today is antimicrobial resistance. Pseudomonas aeruginosa is considered one of the most prevalent causes of healthcare-associated illnesses. Objective: The use of phages as a potential alternative therapeutic method for treating bacterial infections has been the subject of significant research to solve this predicament. Methods: The P. aeruginosa isolates that caused infections of burn wounds were collected from three hospitals in Baghdad. The VITEK system diagnosed and examined the isolates for their antibiotic sensitivity. As well as the phages were isolated and purified from sewage water samples from different sewer stations. After these steps, phages were examined by transmission electron microscopy to ensure the order and the families of these phages, and the tests of lytic activities of phages on the P. aeruginosa isolates were done to determine the best lytic ability of them to use for treatment wounds infections. Results: Burn-wound infections swabs culture showed a positive culture for 109 isolates as the following P. aeruginosa in 76(69.72%), Staphylococcus aureus16(14.67%), Acinetobacter baumannii 9(8.25%), Klebsiella pneumoniae 5(4.58%), Escherichia .coli 3(2.75%) obtained from burn wound infections, had a significant level of resistance to several antibiotics. Four bacteriophages were isolated: KM1, KM2, KM3, KM4 and KM5. KM5 is a mixture of phages referred to as a cocktail. The four phages and their cocktail exhibited significant lytic activity against P. aeruginosa. A total of four monophages and a cocktail were utilized in the preparation of the PEG-phage ointment. Conclusions: All monophages and their cocktail could completely clear bacterial infection in 17 days. KM3 and KM4 PEG-phage ointments demonstrated more potent healing activity than standard, which required 12 days for full recovery, while the cocktail took 15 days (KM5). However, KM1 and KM2 took the same time as the standard, which is 17 days for burn wound infection treated with PEG-phage ointments, while the control, which did not receive any treatment, took 23 days to recover. KM1 and KM2 show less activity for healing burn wound infections from all five phage ointments.

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崔, 欧阳. "Analysis of Credit Risk Measurement of Commercial Banks Based on the ESG-KMV Model." World Economic Research 14, no. 03 (2025): 416–31. https://doi.org/10.12677/wer.2025.143043.

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LUO Changqing, and OUYANG Zisheng. "Hybridizing Multivariate Discriminant Analysis, KMV Model and Support Vector Machine for Credit Risk Measurement." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 20 (2012): 443–52. http://dx.doi.org/10.4156/aiss.vol4.issue20.53.

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Zhang, Daijun, and Huiling He. "Local Government Debt Credit risk and Safe Debt Scale Based on the KMV Model." Open Cybernetics & Systemics Journal 8, no. 1 (2014): 1261–65. http://dx.doi.org/10.2174/1874110x01408011261.

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张, 靖维. "Impact Study of Climate Change on Bank Credit Risk—Based on the KMV Model." E-Commerce Letters 13, no. 03 (2024): 7306–16. http://dx.doi.org/10.12677/ecl.2024.133899.

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刘, 焓杕. "Comparative Study on Credit Risk of Listed Companies in China Based on KMV Model." E-Commerce Letters 13, no. 03 (2024): 8341–51. http://dx.doi.org/10.12677/ecl.2024.1331022.

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Cheng, Xusheng, Ziran Sun, and Weiqi Bao. "Study on credit risk of real estate industry based on genetic algorithm KMV model." Journal of Physics: Conference Series 1629 (September 2020): 012072. http://dx.doi.org/10.1088/1742-6596/1629/1/012072.

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Yeh, Ching-Chiang, Fengyi Lin, and Chih-Yu Hsu. "A hybrid KMV model, random forests and rough set theory approach for credit rating." Knowledge-Based Systems 33 (September 2012): 166–72. http://dx.doi.org/10.1016/j.knosys.2012.04.004.

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46

黄, 昌鑫. "Credit Risk Assessment of Listed Companies in China Based on the Modified KMV Model." Operations Research and Fuzziology 14, no. 02 (2024): 332–39. http://dx.doi.org/10.12677/orf.2024.142139.

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KOUZEZ, MARC, and Danielle Lecointre-Erickson. "Does crisis affect credit risk in developing countries?" International Conference on Advances in Business, Management and Law (ICABML) 2, no. 1 (2019): 47–59. http://dx.doi.org/10.30585/icabml-cp.v2i1.218.

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At the core of the crisis marked by its magnitude, credit risk turned to become a powerful catalyst. The objective of this paper is mainly to follow up the evolution of the credit risk at the Jordanian market during the recent economic and financial international crisis. Based on the linear discriminant model Z-Score and KMV structural model, we recognize the increase in the credit risk during the crisis period. On the whole, the confrontation between models highlights the robust correlation between the accounting results of a company and its market value.

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Shah, Paresh, and Sonal Dave. ""Financial Distress Evaluation of Indian Steel Companies Based On KMV Merton Default to Distance Model"." SKIPS Anveshan 3, no. 1 (2022): 46–55. http://dx.doi.org/10.53031/skips.3.1.2022.04.

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穆, 轩. "Research on Credit Risk Measurement of Listed Brokerage Companies in China Based on KMV Model." E-Commerce Letters 13, no. 03 (2024): 5355–64. http://dx.doi.org/10.12677/ecl.2024.133658.

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Zhou, Tingting, and Guangping Hui. "Credit Risk Analysis of Local Government Financing Platform – An empirical study based on KMV model." SHS Web of Conferences 17 (2015): 01010. http://dx.doi.org/10.1051/shsconf/20151701010.

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