Open Access Paper
23 May 2005 From default probabilities to credit spreads: credit risk models explain market prices (Keynote Address)
Stefan M. Denzler, Michel M. Dacorogna, Ulrich A. Muller, Alexander J. McNeil
Author Affiliations +
Proceedings Volume 5848, Noise and Fluctuations in Econophysics and Finance; (2005) https://doi.org/10.1117/12.618937
Event: SPIE Third International Symposium on Fluctuations and Noise, 2005, Austin, Texas, United States
Abstract
Credit risk models like Moody's KMV are now well established in the market and give bond managers reliable default probabilities for individual firms. Until now it has been hard to relate those probabilities to the actual credit spreads observed on the market for corporate bonds. Inspired by the existence of scaling laws in financial markets by Dacorogna et al. 2001 and DiMatteo et al. 2005 deviating from the Gaussian behavior, we develop a model that quantitatively links those default probabilities to credit spreads (market prices). The main input quantities to this study are merely industry yield data of different times to maturity and expected default frequencies (EDFs) of Moody's KMV. The empirical results of this paper clearly indicate that the model can be used to calculate approximate credit spreads (market prices) from EDFs, independent of the time to maturity and the industry sector under consideration. Moreover, the model is effective in an out-of-sample setting, it produces consistent results on the European bond market where data are scarce and can be adequately used to approximate credit spreads on the corporate level.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stefan M. Denzler, Michel M. Dacorogna, Ulrich A. Muller, and Alexander J. McNeil "From default probabilities to credit spreads: credit risk models explain market prices (Keynote Address)", Proc. SPIE 5848, Noise and Fluctuations in Econophysics and Finance, (23 May 2005); https://doi.org/10.1117/12.618937
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KEYWORDS
Data modeling

Motion models

Monte Carlo methods

Statistical modeling

Solids

Statistical analysis

Mathematical modeling

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