Analyzing Bank Ratings: Key Determinants and Procyclicality

Laere, Elisabeth Van; Baesens, Bart

doi:10.2139/ssrn.1905443

Cited by 2 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And in the other hand, the comparison between the CRA based on econometric studies related to theirs practiced methodologies, to the investigation of the rating determinants and/or the importance of some factors and variables in the rating process using ratings of corporate (Ederington, 1985), municipalities (Moon & Stotsky, 1993), insurances (Pottier & Sommer, 1999), BCRR (Bissoondoyal-Bheenick & Treepongkaruna, 2011;Van Laere & Baesens, 2011;Van Laere, Vantieghem, & Baesens, 2012;Salvador et al, 2014) and sovereignties (Cantor & Packer, 1996;Sehgal et al, 2018;Cuadros-Solas & Salvador, 2021), to name but a few. They showed total convergence in some studies and a partial divergence in others confirming the existence of some subjectivity in the rating attribution.…”

Section: The Literature Reviewmentioning

confidence: 99%

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

Damak

2021

IJFR

View full text Add to dashboard Cite

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.

show abstract

Section: The Literature Reviewmentioning

confidence: 99%

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

Damak

2021

IJFR

View full text Add to dashboard Cite

show abstract

“…The reduced number of research on modeling and forecasting the BCRR (Salvador et al, 2014) concerned mainly the "stand-alone" ratings: «Bank Financial Strength Ratings "BFSR"» of Moody"s (Poon, Firth, & Fung, 1999;Laruccia & Revoltella, 2000;Peresetsky & Karminsky, 2011;Ö güt, Doğanay, Ceylan, &Aktaş, 2012 andSalvador et al, 2014) et «Bank Viability Rating "BVR"» (previously called "Individual Rating "IR"") of FitchRatings (Hammer, Kogan, &Lejeune, 2012 andSalvador et al, 2014). Since 2011, researchers have started to be interested in the "all-in" ratings (Van Laere & Baesens, 2011;Bissoondoyal-Bheenick & Treepongkaruna, 2011;Peresetsky & Karminsky, 2011;Chen, 2012;Van Laere, Vantieghem, & Baesens, 2012;Shen, Huang, & Hasan, 2012;Orsenigo & Vercellis, 2013;Salvador et al, 2014 andGogas, Papadimitriou, &Agrapetidou, 2014). The issuing ratings used by Pagratis and Stringa (2009)…”

Section: The Literature Reviewmentioning

confidence: 99%

“…The second way is to go with a relatively smaller set of variables over factors predefined based on previous studies and use the methods of analysis directly on these variables (Pagratis & Stringa, 2009;Van Laere & Baesens, 2011;Bissoondoyal-Bheenick & Treepongkaruna, 2011;Peresetsky & Karminsky, 2011;Chen, 2012;Van Laere et al, 2012;Shen et al, 2012;Salvador et al, 2014;Gogas et al, 2014). Some studies have used the stepwise procedure to select the best model (Laruccia & Revoltella, 2000;Godlewski, 2004;Van Laere & Baesens, 2011;Van Laere et al, 2012;Distinguin, Hasan, & Tarazi, 2013;and Gogas et al, 2014).…”

Section: The Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

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

Damak¹

2018

IJEF

View full text Add to dashboard Cite

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.

show abstract

Analyzing Bank Ratings: Key Determinants and Procyclicality

Cited by 2 publications

References 27 publications

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

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

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

Contact Info

Product

Resources

About