The Empirical Relation between Credit Quality, Recovery and Correlation
AbstractCredit risk is an important issue in many finance areas, such as the determination of cost of capital, the valuation of corporate bonds and pricing of credit derivatives. Credit risk has also been a cause and consequence of the current financial crisis. Thus, methods for measuring credit risk, default probabilities, and recoveries have caught more and more attention in the financial literature. The majority of industry credit portfolio risk models, as well as recent scientific results, are based on isolated modules for default probabilities and recoveries in the event of default. This paper shows that these common methods lead to various econometric drawbacks when the parameters are interpreted and aggregated for risk capital allocation and pricing purposes. This paper provides a top down approach in which individual credit risk parameters are derived analytically from a single model. This model allows for a i) dynamic, ii) consistent, and iii) unbiased modeling of credit portfolio risks. An empirical analysis provides evidence for the inferred relationship between credit quality, recovery and correlation.
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Bibliographic InfoPaper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-418.
Length: 39 pages
Date of creation: Jun 2009
Date of revision:
Asset Value; Correlation; Credit Portfolio; Loss Given Default; Merton Model; Probability of Default; Recovery; Volatility;
Other versions of this item:
- Daniel Rosch & Harald Scheule, 2009. "The Empirical Relation between Credit Quality, Recovery, and Correlation," Working Papers 222009, Hong Kong Institute for Monetary Research.
- G20 - Financial Economics - - Financial Institutions and Services - - - General
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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