Ultimate recovery mixtures

Edward Altman and Egon A. Kalotay

Journal of Banking & Finance, 2014, vol. 40, issue C, 116-129

Abstract: We propose a relatively simple, accurate and flexible approach to forecasting the distribution of defaulted debt recovery outcomes. Our approach is based on mixtures of Gaussian distributions, explicitly conditioned on borrower characteristics, debt instrument characteristics and credit conditions at the time of default. Using Moody’s Ultimate Recovery Database, we show that our mixture specification yields more accurate forecasts of ultimate recoveries on portfolios of defaulted loans and bonds on an out-of-sample basis than popular regression-based estimates. Further, the economically interpretable outputs of our model provide a richer characterization of how conditioning variables affect recovery outcomes than competing approaches. The latter benefit is of particular importance in understanding shifts in the relative likelihood of extreme recovery outcomes that tend to be realized more frequently than observations near the distributional mean.

Keywords: Bankruptcy; Ultimate recovery; Loss given default; Credit risk; Mixtures of distributions; Defaulted debt (search for similar items in EconPapers)
JEL-codes: G17 G21 G28 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378426613004421
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:40:y:2014:i:c:p:116-129

DOI: 10.1016/j.jbankfin.2013.11.021

Access Statistics for this article

Journal of Banking & Finance is currently edited by Ike Mathur

More articles in Journal of Banking & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().