Retail exposure at default (EAD) is one of the weakest areas of risk measurement and modeling in industry practices and in academic literature. The U.S. Basel II Final Rule is not specific about the approach to EAD. In this study, we use borrower and account information from a large national sample of unsecured credit card defaults to capture borrower and lender behavior as borrowers approach default and to measure and model loan equivalent (LEQ), a common approach to EAD estimation. Dynamic snapshots of account credit ...
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Retail exposure at default (EAD) is one of the weakest areas of risk measurement and modeling in industry practices and in academic literature. The U.S. Basel II Final Rule is not specific about the approach to EAD. In this study, we use borrower and account information from a large national sample of unsecured credit card defaults to capture borrower and lender behavior as borrowers approach default and to measure and model loan equivalent (LEQ), a common approach to EAD estimation. Dynamic snapshots of account credit limits and balances indicate that borrowers are more active than lenders in the "race to default." We find that a little over a dozen borrower, account, and macro factors are significant drivers of EAD. Models incorporating these risk drivers show improved predictive accuracy. Our study offers a useful benchmark to banks' EAD models and fills the void in the literature on retail EAD.
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