Modeling Corporate Exposure at Default
35 Pages Posted: 18 Mar 2013
Date Written: March 18, 2013
Abstract
While the Basel II capital requirements allow considerable flexibility to banks in choosing models for estimating exposure at default (EAD), it is unclear how much these internal estimates could be impacted by the choice of modeling approach. In this paper, we implement several estimation approaches using alternative EAD measures, data treatments, risk drivers and modeling techniques on large corporate syndicated lines of credit data from the Shared National Credit over 1997 to 2009, for both public and private firms. Across models, we find that EAD is higher when firm default is harder for banks to anticipate, in particular when obligors have good ratings, line utilization is low and limits are high or when the credit cycle conditions change. We also show, by comparing in-sample and out-of-sample performance of models in predicting net additions to outstanding balance, that modeling choice has a large impact on EAD estimates. Models differ substantially in whether they under or over-predict aggregate net additions, but accuracy is generally poor across models. Our findings extend existing literature on wholesale EAD in important ways that could benefit banks’ modeling efforts and regulatory guidance in this area.
Keywords: exposure at default, EAD model, Basel II parameters, lines of credit, revolvers, syndicated credit, SNC
JEL Classification: G17, G21, G28, G32, G38
Suggested Citation: Suggested Citation
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