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Florian Resch

13 October 2021
The in-house credit assessment systems (ICASs) developed by euro area national central banks (NCBs) are an important source of credit risk assessment within the Eurosystem collateral framework. They allow counterparties to mobilise as collateral the loans (credit claims) granted to non-financial corporations (NFCs). In this way, ICASs increase the usability of non-marketable credit claims that are normally not accepted as collateral in private market repo transactions, especially for small and medium-sized banks that lend primarily to small and medium-sized enterprises (SMEs). This ultimately leads not only to a widened collateral base and an improved transmission mechanism of monetary policy, but also to a lower reliance on external sources of credit risk assessment such as rating agencies. The importance of ICASs is exemplified by the collateral easing measures adopted in April 2020 in response to the coronavirus (COVID-19) crisis. The measures supported the greater use of credit claim collateral and, indirectly, increased the prevalence of ICASs as a source of collateral assessment. This paper analyses in detail the role of ICASs in the context of the Eurosystem’s credit operations, describing the relevant Eurosystem guidelines and requirements in terms of, among other factors, the estimation of default probabilities, the role of statistical models versus expert analysis, input data, validation analysis and performance monitoring. It then presents the main features of each of the ICASs currently accepted by the Eurosystem as credit assessment systems, highlighting similarities and differences.
JEL Code
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
18 February 2016
When back-testing the calibration quality of rating systems two-sided statistical tests can detect over- and underestimation of credit risk. Some users though, such as risk-averse investors and regulators, are primarily interested in the underestimation of risk only, and thus require one-sided tests. The established one-sided tests are multiple tests, which assess each rating class of the rating system separately and then combine the results to an overall assessment. However, these multiple tests may fail to detect underperformance of the whole rating system. Aiming to improve the overall assessment of rating systems, this paper presents a set of one-sided tests, which assess the performance of all rating classes jointly. These joint tests build on the method of Sterne [1954] for ranking possible outcomes by probability, which allows to extend back-testing to a setting of multiple rating classes. The new joint tests are compared to the most established one-sided multiple test and are further shown to outperform this benchmark in terms of power and size of the acceptance region.
JEL Code
C12 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Hypothesis Testing: General
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G24 : Financial Economics→Financial Institutions and Services→Investment Banking, Venture Capital, Brokerage, Ratings and Ratings Agencies