Loss-given-default (“LGD”) is a critical parameter in modeling the credit risk of instruments subject to default risk, in addition to various other facets of credit risk modeling. However, another source of uncertainty in addition to LGD is the time-to-resolution (“TTR”) of the default event, which has been given limited attention in the literature. LGD and TTR are likely to be correlated with each other and both are likely to vary significantly with various recovery modeling risk factors such as collateral characteristics and the macroeconomic environment. As the TTR is often right censored due to a cut-off in the data sample underlying the estimator of the LGD, such estimators not accounting for this may suffer from what is known in the statistics literature as censoring , which in the credit risk modeling literature is known as LGD resolution bias. LGD models not adjusting for resolution bias through omitting a consideration of the distribution of TTR, the standard variety prevalent in the industry, will result in biased estimates when applied to non-defaulted performing instrument. In this study, we propose to address this issue through the simultaneous modeling of the LGD on resolved cases and TTR on both resolved (non-censored) and unresolved (censored) cases. This study empirically investigates the determinants of LGD and TTR through building alternative econometric models on bonds and loans using an extensive sample of most major U.S. defaults in the period 1985-2022. The key finding is that when compared with standard approaches that do not account for resolution bias, our approach has superior fit to the data in terms of out-of-sample performance where the LGD is unresolved at the point of model development. This study extends prior work by modeling LGD by an advanced (yet practical to implement) econometric technique, incorporating the TTR as well the obligor’s complete capital structure characteristics, for a large corporate asset class and rigorously testing the proposed model on an out-of-sample basis.
KeywordsLoss Given DefaultResolution BiasDefault RiskCredit RiskModel Validation
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