2017
DOI: 10.3390/risks5040052
|View full text |Cite
|
Sign up to set email alerts
|

The Impact of Risk Management in Credit Rating Agencies

Abstract: An empirical study was conducted to determine the impact of different types of risk on the performance management of credit rating agencies (CRAs). The different types of risks were classified as operational, market, business, financial, and credit. All these five variables were analysed to ascertain their impact on the performance of CRAs. In addition, apart from identifying the significant variables, the study focused on setting out a structured framework for future research. The five independent variables w… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

1
2
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(3 citation statements)
references
References 54 publications
1
2
0
Order By: Relevance
“…In compliance with the findings of this article, empirical results have proven that sovereign rating is a well applicable, complex, forward-looking measure of credit risk, and provides important input to sovereign portfolio risk management as shown inter alia in earlier empirical studies by Cantor and Packer (1996), Hu et al (2002), Wei (2003), Kiefer and Larson (2004), Altman and Rijken (2004), Perilioglu and Tuysuz (2015), Seetharaman et al (2017), Kleszcz andNehrebecka (2020), andde Oliveira et al (2021).…”
Section: Discussionsupporting
confidence: 75%
“…In compliance with the findings of this article, empirical results have proven that sovereign rating is a well applicable, complex, forward-looking measure of credit risk, and provides important input to sovereign portfolio risk management as shown inter alia in earlier empirical studies by Cantor and Packer (1996), Hu et al (2002), Wei (2003), Kiefer and Larson (2004), Altman and Rijken (2004), Perilioglu and Tuysuz (2015), Seetharaman et al (2017), Kleszcz andNehrebecka (2020), andde Oliveira et al (2021).…”
Section: Discussionsupporting
confidence: 75%
“…To be able to compare the results between the algorithms, we need to verify that we use the same variables to get the outputs one or zero. If that is not the case, the comparison will be difficult and could be biased Seetharaman et al (2017).…”
Section: The Criteriamentioning
confidence: 99%
“…e difference between big data credit investigation and traditional credit investigation is as follows. e traditional credit investigation is involved with the following: structured data such as credit data, mobile phone bills, and consumer bills, serving the credit market, targeting the assets and debts of large enterprises and personal credit subjects with relatively complete credit records, and circumstances and ability to repay [19,20]. Big data credit investigation is involved with the following: unstructured data, such as network data focusing on serving inclusive finance, and targeting small and microenterprises and individuals without credit records and paying attention to some social behaviors of credit subjects, such as circle of friends, consumer preferences, and online search records and many more [21,22].…”
Section: Consumer Finance Credit Risk Management Experiments Based On Big Data Credit Investigationmentioning
confidence: 99%