Modelling the economic value of credit rating systems

Jankowitsch, Rainer; Pichler, Stephan; Schwaiger, Walter S. A.

doi:10.1016/j.jbankfin.2006.01.003

Cited by 30 publications

(9 citation statements)

References 11 publications

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“…As other surveys like Jankowitsch, Pichler and Schwaiger (2007) show, investment in the rating system can bring not only a reduction in Basel's required capital held in the bank but also a reduction in credit losses.…”

Section: Discussionmentioning

confidence: 88%

“…The size of the rated company (bank's client) and its importance for the rating result is one of the topics I deal with in this paper. Jankowitsch, Pichler and Schwaiger (2007) analyze the potential economic value of improving the credit rating system. They show, that an investment in the rating system can bring not only a reduction in Basel's required capital held in the bank but also a reduction in credit losses by increasing the annual rate of return by 30-40 bps.…”

Section: Review Of Literaturementioning

confidence: 99%

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Banking Rating Systems for IRB: Comparison of Three Systems from German-speaking Environment

Svítil

2017

FAI

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The aim of the article is to compare several rating systems used by banks and their affiliates, especially in German-speaking countries. The research is focused on the rating of business entities, more precisely the corporate ones, (especially limited liability companies or joint-stock companies). In particular, two aspects of the rating of the corporations are highlighted in the rating system comparison, namely: (i) which quantitative indicators are used for the calculation of the hard-facts and (ii) how the soft-facts are included in the rating system. The result of the research shows, that concerning the quantitative indicators (i), the highest emphasis is put on the capital structure of an enterprise (whether in the form of Equity to Liabilities or Equity to Total assets) in all compared rating systems. Concerning the other indicators used for the calculation, the monitored rating systems are different, and the total number of indicators also differs (from 6 to 9). For soft-facts (ii) all rating systems agree on some of the queries (e.g. the sensitivity of the rated entity to market fluctuations), but otherwise the number and the topics of the queries overlap only partially.

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Section: Discussionmentioning

confidence: 88%

Section: Review Of Literaturementioning

confidence: 99%

Banking Rating Systems for IRB: Comparison of Three Systems from German-speaking Environment

Svítil

2017

FAI

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“…The authors found that an informal credit system between friends reduced credit fraud. Jankowitsch et al 2007 developed a model of the economic value of credit rating systems based on statistical economic models. Brown and Zehnder 2010 employed game theory and provided a systematic analysis of information sharing mechanisms between lenders in the credit market with asymmetric information and competition.…”

Section: Theoretical Foundationmentioning

confidence: 99%

“…The research approaches show that existing studies can be categorized according to two main schools of thought -traditional and emerging credit theories. While the former investigates credit theory based on various economics theories, e.g., institutional economics, information economics, and game theory (Stiglitz and Weiss 1981;Jarrow and Xu 2010;Brown and Zehnder 2010), the emerging studies utilize various current experimental techniques for complex systems, such as multi-agent-based models, system dynamics, and other simulation methods (Jankowitsch et al 2007;Barnaud et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Social credit: a comprehensive literature review

Tang

et al. 2015

Financial Innovation

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To avoid credit fraud, social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs. This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation, scoring methods, and regulatory mechanisms. The study considers the credit of various economic agents within the social credit system such as countries (or governments), corporations, and individuals and their credit variations in online markets (i.e., network credit). A historical review of the theoretical (or model) development of economic agents is presented together with significant works and future research directions. Some interesting conclusions are summarized from the literature review. (1) Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms. (2) The most popular credit scoring methods include expert systems, econometric models, artificial intelligence (AI) techniques, and their hybrid forms. Evaluation indexes should vary across different target agents. (3) The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.

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“…For instance, rating bias between commercial banks using the internal-ratings-based (IRB) approach to calculate capital requirements leads to a systematic deviation of one-year PD estimates, see Hornik et al (2007b). This can bias regulatory capital and borrower selection if the bank uses the estimates in the pricing of loans as well (Jankowitsch et al, 2007).…”

Section: Intuitionmentioning

confidence: 99%

Re-Mapping Credit Ratings

2011

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Rating agencies report ordinal ratings in discrete classes. We question the market's implicit assumption that agencies define their classes on identical scales, e.g., that AAA by Standard & Poor's is equivalent to Aaa by Moody's. To this end, we develop a non-parametric method to estimate the relation between rating scales for pairs of raters. For every rating class of one rater this, scale relation identifies the extent to which it corresponds to any rating class of another rater, and hence enables a rating-class specific re-mapping of one agency's ratings to another's. Our method is based purely on ordinal co-ratings to obviate error-prone estimation of default probabilities and the disputable assumptions involved in treating ratings as metric data. It estimates all rating classes' relations from a pair of raters jointly, and thus exploits the information content from ordinality.We find evidence against the presumption of identical scales for the three major rating agencies Fitch, Moody's and Standard & Poor's, provide the relations of their rating classes and illustrate the importance of correcting for scale relations in benchmarking.

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Modelling the economic value of credit rating systems

Cited by 30 publications

References 11 publications

Banking Rating Systems for IRB: Comparison of Three Systems from German-speaking Environment

Banking Rating Systems for IRB: Comparison of Three Systems from German-speaking Environment

Social credit: a comprehensive literature review

Re-Mapping Credit Ratings

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