Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond |
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| Affiliation: | 1. Fordham University, Graduate School of Business Administration, 113 West 60th Street, New York, NY 10023, United States;2. Financial Engines Inc., Sunnyvale CA, 94089, United States;1. Department of Neurosurgery, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, PR China;2. State Key Laboratory of Cancer Biology, The Fourth Military Medical University, Xi’an 710032, PR China;3. Department of Pharmacogenomics, School of Pharmacy, The Fourth Military Medical University,Xi''an 710032, PR China;4. Department of Surgery, Boston Veterans Affairs Healthcare System, Boston University School of Medicine, Boston, MA 02130, USA;1. Louisiana State University, USA;2. The University of Miami, USA;1. Reserve Bank of Australia, 65 Martin Pl, Sydney, NSW, Australia;2. University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK;3. Centre for Risk Studies, Cambridge Judge Business School, University of Cambridge, Trumpington St Cambridge CB2 1AG, UK |
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| Abstract: | This study investigates the impacts of unobservable firm heterogeneity on modelling corporate bond recovery rates at the instrument level. Based on the recovery information over a long horizon from 1986 to 2012, we find that an obligor-varying linear factor model presents significant improvements in explaining the variations of recovery rates with a remarkably high intra-class correlation being observed. It emphasizes that the inclusion of an obligor-varying random effect term has effectively explained the unobservable firm level information shared by instruments of the same issuer and thus results in an improvement of predictive accuracy of recovery rates. The empirical results show that the latent economic cyclical effects have been well represented by firm level heterogeneity, and strong evidence is presented for the normal distributional assumption of the recovery rates. Finally, we demonstrate the choice of recovery rate models may influence portfolio risk with the obligor-varying factor model generating a more right clustered loss distribution than other regression methods on the aggregated portfolio. |
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| Keywords: | Unobservable heterogeneity Loss given default Portfolio loss distribution |
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