scholarly journals Bank Credit Risk Rating Process: Is There a Difference Between Agencies?

2021 ◽  
Vol 12 (5) ◽  
pp. 194
Author(s):  
Emna Damak
Keyword(s):  
Credit Risk ◽  
Multiple Regression ◽  
Credit Rating ◽  
Bank Credit ◽  
Credit Rating Agency ◽  
Credit Quality ◽  
Logit Regression ◽  
Risk Rating ◽  
Rating Process ◽  
Relevant Variables

The purpose of this article is to compare the bank credit risk rating (BCRR) process between credit rating agency (CRA) after the 2012 revision of their methodologies using 76 banks from 23 EMENA countries rated simultaneously by S&P's, Moody's and FitchRatings. We made this comparison based on the CAMELS model with a proposed 'S’ to BCRR. We use “ordered logit” regression for the rating classes and we complete our analysis by “linear multiple” regression for the rating grades. The results show that the BCRR processes are largely consistent between agencies but not aligned. Some differences appear in the important factors and relevant variables of the intrinsic credit quality component that manifest themselves in specific behaviors distinguishing one agency to another. The three agencies agree on the factors: Capital, Earnings, Liquidity and Supports and the most relevant support variable is the sovereign rating of the bank's country of establishment. The results also confirm a consistence between the BCRR's revealed and practiced methodologies revised by the CRA.

scholarly journals Bank Credit Risk Rating Process: Is There a Change With the 2007-09 Crisis?

2021 ◽  
Vol 12 (5) ◽  
pp. 41
Author(s):  
Emna Damak
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Lessons Learned ◽  
Bank Credit ◽  
Rating Agencies ◽  
Risk Rating ◽  
Rating Process ◽  
Before And After ◽  
Relevant Variables ◽  
Rating Changes

The purpose of this article is to study empirically the bank credit risk rating (BCRR) process over time using 89 banks from 27 EMENA countries rated by S&P’s simultaneously before and after 2007-09 crises. We made this comparison based on the CAMELS model with a proposed ‘S’ to BCRR. We use "ordered logit" regression for the rating classes and we complete our analysis by “linear multiple” regression for the rating grades. The results show that the rating changes in 2012 are mainly a methodology revision consequence of the entire rating process changes, including the weight of components, the important factors and the relevant variables in order to take into account some of the lessons learned from this global crisis. They also show a consistence between the BCRR's revealed and practiced methodologies revised by the credit rating agencies (CRAs).

scholarly journals CAMELS Model With a Proposed ‘S’ for the Bank Credit Risk Rating

2018 ◽  
Vol 10 (9) ◽  
pp. 69
Author(s):  
Emna Damak
Keyword(s):  
Credit Risk ◽  
Financial Statements ◽  
Bank Credit ◽  
Bootstrap Resampling ◽  
Logit Regression ◽  
Risk Rating ◽  
S Factor ◽  
Relevant Variables ◽  
Factor Determine ◽  
The Stability

The purpose of this article is to adopt the CAMELS model to the bank credit risk rating by using simple indicators from publicly available quantifiable information retrieval from their financial statements. Then, it is to test its empirical validation after completion of its revised methodology in 2012 as response to the sub-prime crisis using the rating ‘all-in’ of 128 banks rated by Moody’s of 29 EMENA countries. We use ‘ordered logit’ regression for the variable to explain the rating classes and the bootstrap resampling techniques to assess the stability degree of the best model selected with the information criteria’s AIC. Under this scheme, the explanatory powers measured by Pseudo R2 of the best model is 56.47%. The results show that the two components: intrinsic credit quality and the support of the environment measured respectively by CAMEL factors and the proposed ‘S’ factor determine well the ‘all-in’ ratings. The sovereign rating of the bank establishment country, the size and the ‘stand-alone’ rating of the bank are the most relevant variables.

Bank Credit Risk Analysis and Bank Credit Rating

2020 ◽  
pp. 275-348
Author(s):  
Terence M. Yhip ◽  
Bijan M. D. Alagheband
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Credit Rating ◽  
Bank Credit ◽  
Credit Risk Analysis

scholarly journals Empirical evolution of credit risk over a decade in IBEX. 35 companies and its relationship with the qualification of its ratings

2020 ◽  
Vol 16 (2) ◽  
pp. 61
Author(s):  
Josep Patau
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Clear Correlation ◽  
Crisis Period ◽  
Rating Agencies ◽  
Credit Rating Agency ◽  
Rating Agency ◽  
Determining Factors ◽  
Commercial Loans ◽  
The One

Object: The present work responds to two objectives. On the one hand, it describes the evolution of the main economic-financial indicators that influence credit risk (insolvency) for a sample of 10 Spanish companies listed on the IBEX 35. This analysis is studied for a comparative period of 10 years, which coincides with a pre-crisis stage (2002-2005) and an economic post-crisis phase (2012-2015). On the other hand, it corroborates the relationship between the analysed insolvency and the rating or credit-risk rating published for these companies by an internationally recognized credit rating agency, Standard & Poor's (S & P).Design / methodology: A sample of 10 companies and a 10-year period including the years 2002-2005 (pre-crisis) and the years 2012-2015 (post-crisis) are chosen, omitting the Spanish economic crisis that occurred in the year 2008. For the study of its evolution, 6 ratios obtained from the scientific literature that relate to credit risk and its effects on investments and company results are calculated. Finally, the correlations of these variables with the ratings of credit risk assessment by the rating agency S & P are measured. Descriptive statistics will assign value and graphics to this ten-year evolution, and with the incorporation of a factorial analysis, the correlation between the ratios and the S & P rating will be determined. The statistical analysis explains this correlation to a greater extent.Contributions / results: The results show a clear increase in the value of the impairment variable due to credit risk ten years later that directly affects the results of the companies, despite these companies having significantly reduced their investments in commercial loans pending collection and drastically reduced the period means of collection of clients. In turn, there is a clear correlation between the insolvency studied and the variables used by the S & P rating agency for the assessment of credit risk.Added value / conclusions: The empirical study concludes that there is a correspondence between insolvency and the rating given by an internationally prestigious rating agency (S & P) for the sample of 10 companies studied. Three variables – customer balance-accounts receivable, investments and the net amount of turnover – are determining factors explaining this correlation, and these three variables are the same ones that decisively influence both the pre-crisis period and the post-crisis period 10 years apart. The rating agencies weigh the insolvency variable in their analyses.

The impact of market sectors and rating agencies on credit ratings: global evidence

2019 ◽  
Vol 20 (5) ◽  
pp. 389-410
Author(s):  
Kerstin Lopatta ◽  
Magdalena Tchikov ◽  
Finn Marten Körner
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Content Type ◽  
Rating Agencies ◽  
Credit Rating Agency ◽  
Rating Agency ◽  
Investment Grade ◽  
The Impact ◽  
Rating Downgrades

Purpose A credit rating, as a single indicator on one consistent scale, is designed as an objective and comparable measure within a credit rating agency (CRA). While research focuses mainly on the comparability of ratings between agencies, this paper additionally questions empirically how CRAs meet their promise of providing a consistent assessment of credit risk for issuers within and between market segments of the same agency. Design/methodology/approach Exhaustive and robust regression analyses are run to assess the impact of market sectors and rating agencies on credit ratings. The examinations consider the rating level, as well as rating downgrades as a further measure of empirical credit risk. Data stems from a large global sample of Bloomberg ratings from 11 market sectors for the period 2010-2018. Findings The analyses show differing effects of sectors and agencies on issuer ratings and downgrade probabilities. Empirical results on credit ratings and rating downgrades can then be attributed to investment grade and non-investment grade ratings. Originality/value The paper contributes to current finance research and practice by examining the credit rating differences between sectors and agencies and providing assistance to investors and other stakeholders, as well as researchers, how issuers’ sector and rating agency affiliations act as relative metrics.

A Bayesian inference model for the credit rating scale

2016 ◽  
Vol 17 (4) ◽  
pp. 390-404 ◽  
Author(s):  
Philipp Gmehling ◽  
Pierfrancesco La Mura
Keyword(s):  
Bayesian Inference ◽  
Credit Risk ◽  
Credit Rating ◽  
Rating Scale ◽  
Credit Rating Agencies ◽  
Model Framework ◽  
Content Type ◽  
Rating Agencies ◽  
Credit Rating Agency ◽  
Limited Accuracy

Purpose This paper aims to provide a theoretical explanation of why credit rating agencies typically disclose credit risk of issuers in classes rather than publishing the qualitative ranking those classes are based upon. Thus, its goal is to develop a better understanding of what determines the number and size of rating classes. Design/methodology/approach Investors expect ratings to be sufficiently accurate in estimating credit risk. In a theoretical model framework, it is therefore assumed that credit rating agencies, which observe credit risk with limited accuracy, are careful in not misclassifying an issuer with a lower credit quality to a higher rating class. This situation is analyzed as a Bayesian inference setting for the credit rating agencies. Findings A disclosure in intervals, typically used by credit rating agencies results from their objective of keeping misclassification errors sufficiently low in conjunction with the limited accuracy with which they observe credit risk. The number and size of the rating intervals depend in the model on how much accuracy the credit rating agencies can supply. Originality/value The paper uses Bayesian hypothesis testing to illustrate the link between limited accuracy of a credit rating agency and its disclosure of issuers’ credit risk in intervals. The findings that accuracy and the objective of avoiding misclassification determine the rating scale in this theoretical setting can lead to a better understanding of what influences the interval disclosure of major rating agencies observed in practice.

scholarly journals A Dependent Hidden Markov Model of Credit Quality

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Małgorzata Wiktoria Korolkiewicz
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Rating ◽  
Hidden Markov ◽  
Credit Ratings ◽  
The State ◽  
Credit Quality ◽  
The Em Algorithm ◽  
Rating Process

We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.

scholarly journals Bank Credit Strategy Model Based on AHP-Fuzzy Comprehensive Evaluation

2021 ◽  
Vol 7 (1) ◽  
pp. 22
Author(s):  
Zhongbin Fang ◽  
Hongru Fan ◽  
Haoxin Huang ◽  
Yuhan Zhuang ◽  
Jia Ji ◽  
...  
Keyword(s):  
Interest Rate ◽  
Credit Risk ◽  
Credit Rating ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Supply And Demand ◽  
Fuzzy Comprehensive Evaluation ◽  
Bank Credit ◽  
Strategic Issues ◽  
Demand Chain

Credit risk control and credit strategy formulation of medium and micro enterprises have always been important strategic issues faced by commercial banks. Banks usually make corporate loan policies based on the credit degree, the information of trading bills and the relationship of supply-demand chain of the enterprise. In this paper, we established the AHP-Fuzzy comprehensive evaluation model for quantifying enterprise credit risk. Based on the relevant data of 123 enterprises with credit records, the credit strategy is formulated according to the three indicators of enterprise strength, enterprise reputation and stability of supply-demand relationship. This paper also combines the credit reputation, credit risk and supply and demand stability rating in order to establish the bank credit strategic planning model to decide whether to lend or not and the lending order. The conclusion shows that, under the condition of constant total loan amount, the enterprises with the highest credit rating should be given priority. Then, combined with the change of customer turnover rate with interest rate, we take the bank's maximize expected income as objective to calculate the optimal loan interest rate of different customer groups.

Do asset sales affect firm credit risk? – Evidence from credit rating assignments

2014 ◽  
Vol 40 (9) ◽  
pp. 903-927 ◽  
Author(s):  
Vinod Venkiteshwaran
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Ordered Probit ◽  
Asset Sales ◽  
Content Type ◽  
Credit Quality ◽  
The One ◽  
Opposing Effects ◽  
The Impact

Purpose – Asset sales can have opposing effects on firm credit quality. On the one hand asset sales could signal increased credit risk resulting from distress or on the other hand they could improve internal liquidity and hence credit quality. Therefore the impact potential asset sales can have on credit quality is an empirical question and one that has previously not been examined in the literature. The paper aims to discuss these issues. Design/methodology/approach – Using credit ratings as a measure of firm credit quality, in ordered probit regressions, this study finds evidence consistent with the internal liquidity view of the asset sales-credit risk relationship. Findings – Results from ordered probit regressions of credit ratings show that the likelihood of higher credit ratings is increasing in industry-level turnover of real assets Originality/value – Credit-rating agencies often cite the impact of asset sales on firm credit quality as a motivation for their rating assignments. Distress-driven asset sales could reduce firm credit quality whereas other asset sales could result in increased internal firm liquidity and hence improve firm credit quality. This bi-directional expectation leaves the question of how asset sales affect credit quality to be answered empirically and has not been previously tested in the literature.

scholarly journals Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities

2019 ◽  
Vol 12 (3) ◽  
pp. 124 ◽  
Author(s):  
Takeaki Kariya ◽  
Yoshiro Yamamura ◽  
Koji Inui
Keyword(s):  
Credit Risk ◽  
Term Structure ◽  
Credit Rating ◽  
Default Probability ◽  
Corporate Bond ◽  
Rating System ◽  
Financial Industry ◽  
Cross Sectional ◽  
Risk Rating ◽  
Rating Method

Undoubtedly, it is important to have an empirically effective credit risk rating method for decision-making in the financial industry, business, and even government. In our approach, for each corporate bond (CB) and its issuer, we first propose a credit risk rating (Crisk-rating) system with rating intervals for the standardized credit risk price spread (S-CRiPS) measure presented by Kariya et al. (2015), where credit information is based on the CRiPS measure, which is the difference between the CB price and its government bond (GB)-equivalent CB price. Second, for each Crisk-homogeneous class obtained through the Crisk-rating system, a term structure of default probability (TSDP) is derived via the CB-pricing model proposed in Kariya (2013), which transforms the Crisk level of each class into a default probability, showing the default likelihood over a future time horizon, in which 1545 Japanese CB prices, as of August 2010, are analyzed. To carry it out, the cross-sectional model of pricing government bonds with high empirical performance is required to get high-precision CRiPS and S-CRiPS measures. The effectiveness of our GB model and the S-CRiPS measure have been demonstrated with Japanese and United States GB prices in our papers and with an evaluation of the credit risk of the GBs of five countries in the EU and CBs issued by US energy firms in Kariya et al. (2016a, b). Our Crisk-rating system with rating intervals is tested with the distribution of the ratings of the 1545 CBs, a specific agency’s credit rating, and the ratings of groups obtained via a three-stage cluster analysis.

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