CAMELS-Based Supervision and Risk Management: What Works and What Does Not

Risal, Hari Gopal; Panta, Sabin Bikram

doi:10.1177/2319714519873747

Cited by 7 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality and methods of supervision have improved at central banks worldwide due to dramatic developments. Numerous developed, developing, and underdeveloped nations currently use the CAMEL rating system to evaluate the performance of banks (Gautam, 2020;Gazi et al, 2022;Lavanya & Srinivas, 2018;Naushad, 2021;Risal & Panta, 2019).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Financial Performance of Nepalese Commercial Banks using CAMEL Approach

Sah

Pokharel

2023

Cognition

View full text Add to dashboard Cite

Bank and financial institutions require a method for assessing performance, considering some crucial financial statistics, and identifying strengths and weaknesses. The "CAMEL" model is a effective tool for assessing the performance of bank and financial institution. This model studied Capital adequacy, Asset quality, Management quality, Earnings capacity, and Liquidity condition of sample banks. The study has made a modest attempt to use the CAMEL technique to examine the performance of three commercial banks in Nepal from 2011/12 to 2020/21. Rastriya Banijya Bank, a wholly government-owned, Nepal SBI Bank Limited (NSBIL) joint venture, and Prime Commercial Bank Limited (PCBL), privately owned, were considered as a sample banks for the analysis. The result explained that PCBL and NSBIL could keep their risk-weighted assets at more excellent Tier I and II capital levels, demonstrating their financial soundness. This also revealed that NSBIL could support a higher percentage of well-performing loans. RBBL, NSBIL, and PCBL, all BFIs maintained average returns on shareholder equity and returns on assets. It also found PCBL was first in terms of the ratio of liquid assets to total deposits, followed by NSBIL banks and RBBL. In light of this, this research will be tremendously instructive to academics, researchers, and bank management. They can utilize it to create a financial plan for the effectiveness of the bank performance as a whole.

show abstract

Section: Discussionmentioning

confidence: 99%

Analysis of Financial Performance of Nepalese Commercial Banks using CAMEL Approach

Sah

Pokharel

2023

Cognition

View full text Add to dashboard Cite

show abstract

“…The Wald statistics for the models applied show the p value to be significant at 1% level. This implies the significance of the model estimated and the results / outcomes can be analyzed and interpreted meaningfully (Risal, 2019). It is evident that the commercial banks are safer than development banks as they are less prone to risk captured by both the SDROA and SDROE, yet offer significantly higher ROE and fairly more NIM.…”

Section: Stationarity Testmentioning

confidence: 94%

“…Since, dependent variable is bank performance; ROA, ROE, Volatility of ROA and ROE and NIM; which correlates with their own past demands lagged independent variable. In addition, there may exist some heterogeneity demanding the need of Panel data to overcome the deficiencies of in model estimation which are prevalent in cross sectional and time series dimensions (Risal, 2019). Furthermore, panel data offers more degrees of freedom and less multicollinearity than cross-sectional and time-series data.…”

Section: Review Of Literaturementioning

confidence: 99%

Role of Credit Risk in Performance difference between A and B Class Banks in Nepal

Risal

Poudel

2020

NRB Econ. Rev.

View full text Add to dashboard Cite

This paper explains the performance differences between A and B class financial institutions arising from credit risk. The dynamic panel data from 2008 to 2019 has been considered from all 28 commercial banks and 11 national level development banks for analysis. Arellano Bond method has been performed to control the unobserved heterogeneity and to reduce biasness in the parameter estimation as they have both cross sectional and time dimensions. The results have shown clear differences in credit risk status between A class and B class bank with all the parameters except for Return on Assets (ROA). The results show that the A class commercial banks are less vulnerable than the B class bank as measured by Standard deviation of ROA (standard deviation of return on equity (SDROE) both, yet offer substantially higher ROE and fairly higher NIM. Findings suggest that the past performance BFIs, regardless their classes, are capable enough to predict their future performance as all lag variables are significant. Development banks are advised to focus on maintaining appropriate credit to deposit ratio (CDR) as it has been affecting most of the performance indicators whereas, commercial banks are advised to monitor their loan loss provision to total loans and advances (LLPTLA) for better performance. The control variables have been found to have negligible effect on performance of banks yet higher inflation deteriorates the performance even at a small amount. Further, contradictory findings on influence of real gross domestic product (GDP) growth with the performance demands a need of further research. To recapitulate, the credit risk plays a vital role in performance of banks in Nepal and A class banks safer with returns.

show abstract

“…In recent decades, several studies have been conducted on the use of CAMELS variables in risk measurement and monitoring. Examples can be found in [30]- [33]. The fundamental ideas behind this practical application are embedded in the close relationship between poor performance and financial distress of credit risk: the former may be considered as a proxy for the latter [10].…”

Section: Literature Review a Unveiling The Relationships Among Camels...mentioning

confidence: 99%

Revisiting CAMELS Rating System and the Performance of ASEAN Banks: A Comprehensive MCDM/Z-Numbers Approach

et al. 2022

View full text Add to dashboard Cite

This paper provides a new perspective on the performance of ASEAN member countries' banks as proxied by CAMELS rating system in the light of information reliability. A comprehensive MCDM approach is developed based on alternative methods to handle expert preference uncertainties regarding banking ideal performance levels and relative CAMELS variable efficiency. While expert preferences are collected using structured interviews, the partial bank rankings are defined upon Fuzzy TOPSIS with the primary relative efficiency weights obtained from SWARA. Z-numbers are utilized to address the inherent fuzziness in how banking performance and financial distress are associated with information reliability of positive-ideal banking performance and CAMELS variables efficiency functions generated from expert preferences or perceptions. The empirical findings demonstrated that employing information reliability methodologies applied to a proxy of the CAMELS rating system, the ambiguous influence of ASEAN banking performance on financial hardship can be adequately handled.

show abstract

CAMELS-Based Supervision and Risk Management: What Works and What Does Not

Cited by 7 publications

References 30 publications

Analysis of Financial Performance of Nepalese Commercial Banks using CAMEL Approach

Analysis of Financial Performance of Nepalese Commercial Banks using CAMEL Approach

Role of Credit Risk in Performance difference between A and B Class Banks in Nepal

Revisiting CAMELS Rating System and the Performance of ASEAN Banks: A Comprehensive MCDM/Z-Numbers Approach

Contact Info

Product

Resources

About