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François Coppens

22 May 2017
Analysis of consolidated accounting data of European listed groups shows significant differences in some key ratios between countries However, the figures do not reveal whether these differences result from a distinct composition of the countries’ populations in terms of branches of activity (structural effect) or from intrinsic disparities in the behaviour of groups from various countries. This paper will address this issue using ratio decomposition techniques. A comparative overview of decomposition methodologies available in the literature will be provided, as well as an in-depth description of the methodology used. This will be applied to decompose the difference in the financial debt ratio, the equity ratio and the EBIT margin across countries for one specific year and to consider any dissimilarities in financial debt ratios over a limited period of time. The study will be based on the data available in the ERICA dataset from the European Committee of Central Balance Sheet Data Offices (ECCBSO), which includes accounting data of listed groups from Austria, Belgium, France, Germany, Greece, Italy, Portugal and Spain. The aggregate ratios of each country will be compared against a benchmark composed of the aggregate ratios for the eight countries together.
JEL Code
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
L22 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Organization and Market Structure
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
M4 : Business Administration and Business Economics, Marketing, Accounting→Accounting and Auditing
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
10 July 2007
The aims of this paper are twofold: first, we attempt to express the threshold of a single "A" rating as issued by major international rating agencies in terms of annualised probabilities of default. We use data from Standard & Poor's and Moody's publicly available rating histories to construct confidence intervals for the level of probability of default to be associated with the single "A" rating. The focus on the single "A" rating level is not accidental, as this is the credit quality level at which the Eurosystem considers financial assets to be eligible collateral for its monetary policy operations. The second aim is to review various existing validation models for the probability of default which enable the analyst to check the ability of credit assessment systems to forecast future default events. Within this context the paper proposes a simple mechanism for the comparison of the performance of major rating agencies and that of other credit assessment systems, such as the internal ratings-based systems of commercial banks under the Basel II regime. This is done to provide a simple validation yardstick to help in the monitoring of the performance of the different credit assessment systems participating in the assessment of eligible collateral underlying Eurosystem monetary policy operations. Contrary to the widely used confidence interval approach, our proposal, based on an interpretation of p-values as frequencies, guarantees a convergence to an ex ante fixed probability of default (PD) value. Given the general characteristics of the problem considered, we consider this simple mechanism to also be applicable in other contexts.
JEL Code
G20 : Financial Economics→Financial Institutions and Services→General
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
C49 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Other