Post by Sapphire Capital on Aug 14, 2008 4:15:47 GMT 4
An Empirical Study of Exposure at Default
Michael Jacobs Jr.
OCC/Risk Analysis Division/Credit Risk Modeling
June 21, 2008
Abstract:
In this study we empirically investigate the determinants of and build a predictive econometric model for exposure at default (EAD) using a sample of defaulted firms having revolving credits, at one time having S&P or Moody's rated debt. We extend prior empirical work (Araten et al 2001, Asarnow 1994) by considering alternative determinants (i.e., borrower accounting, industry/macroeconomic and debt/equity market determinants) of EAD risk, in addition to the traditional factors of credit rating, utilization and tenor. Various measures of EAD risk are derived and compared (the loan equivalent-LEQ, credit conversion-CCF and exposure at default-EADF factors). We build a multiple regression model in the generalized linear class for these and examine the comparative rank ordering and predictive accuracy properties of these measures, in which EADF (CCF) performs best (worst). We find weak evidence of counter-cyclicality in EAD. While we find EAD risk to decrease with default risk, with risk rating having some explanatory power, utilization has the strongest inverse relation. We also find EAD risk reduced for greater leverage, liquidity, more debt cushion; and increased for greater company size, higher collateral rank of the loan or more bank debt in the capital structure of the defaulted obligor. The models are validated rigorously through resampling experiment in a rolling out-of-time and out-of-sample experiment.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1149407_code515373.pdf?abstractid=1149407&mirid=1
Michael Jacobs Jr.
OCC/Risk Analysis Division/Credit Risk Modeling
June 21, 2008
Abstract:
In this study we empirically investigate the determinants of and build a predictive econometric model for exposure at default (EAD) using a sample of defaulted firms having revolving credits, at one time having S&P or Moody's rated debt. We extend prior empirical work (Araten et al 2001, Asarnow 1994) by considering alternative determinants (i.e., borrower accounting, industry/macroeconomic and debt/equity market determinants) of EAD risk, in addition to the traditional factors of credit rating, utilization and tenor. Various measures of EAD risk are derived and compared (the loan equivalent-LEQ, credit conversion-CCF and exposure at default-EADF factors). We build a multiple regression model in the generalized linear class for these and examine the comparative rank ordering and predictive accuracy properties of these measures, in which EADF (CCF) performs best (worst). We find weak evidence of counter-cyclicality in EAD. While we find EAD risk to decrease with default risk, with risk rating having some explanatory power, utilization has the strongest inverse relation. We also find EAD risk reduced for greater leverage, liquidity, more debt cushion; and increased for greater company size, higher collateral rank of the loan or more bank debt in the capital structure of the defaulted obligor. The models are validated rigorously through resampling experiment in a rolling out-of-time and out-of-sample experiment.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1149407_code515373.pdf?abstractid=1149407&mirid=1