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

Abstract

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 Rating Process: Is There a Change With the 2007-09 Crisis?

“…Given that the number of observations in our sample is not important for taking a sub-sample, we will test the robustness of our hypotheses by using the bootstrapping approach (Royston & Sauerbrei, 2009) with 1000 replications for regressions of the equations (from 1 to 3). Damak (2018) after a small adjustment by the author of the variables to explain and of control to adapt it to the needs of the study.…”

“…This model is generally accepted in its composite and component form as, expressed by Derviz and Podpiera in 2008, an important monitoring instrument and research topic for those interested in the behavior of banks for academic or applied purposes. But to achieve our goal, we chose an adaptation of this model to the BCRR that explains and reconstructs well the 'all-in' ratings proposed by Damak (2018). It consists of using the CAMELS model with BCRR specific 'S' with simple indicators drawn from publicly available quantifiable information.…”

“…The explanatory variables selected to represent each factor are presented in Table 2. They are extract from the work of Damak (2018): 10 variables of the CAMEL model for "intrinsic credit quality" component and 4 variables for "environmental support" component. All variables of CAMEL factors are three-year averages that precede the rating year (Note 2).…”

“…A theoretical study followed by empirical validation was conducted by Damak (2018). The author attempted to adopt the famous CAMELS (Note 1) model by proposing an adjusted "S" to the BCRR and using simple indicators based on quantifiable information available to the public from their financial statements.…”

On the Lyapunov Matrix of Linear Delay Difference Equations in Continuous Time * *Project CONACYT 180725

“…In Engineering Sciences, motivations to study delay difference equations in continuous time come from sampleddata systems, neutral time-delay systems (see Kharitonov (2013), Hale and Verduyn Lunel (1993), and Fridman (2001)), difference equations with distributed delays (see the examples in Melchor-Aguilar (2013)), conservation laws modeled by first order hyperbolic partial differential equations in which a transport phenomenon occurs, Hale and Verduyn Lunel (1993), and other classes of linear systems with distributed parameters, which have been shown in Damak (2015) to admit a representation in the form of difference equations in continuous time.…”