This paper combines a term structure model of credit default swaps (CDS) with weak-identification robust methods to jointly estimate the probability of default and the loss given default of the underlying firm. The model is not globally identified because it forgoes parametric time series restrictions that have aided identification in previous studies, but that are also difficult to verify in the data. The empirical results show that informative (small) confidence sets for loss given default are estimated for half of the firm-months in the sample, and most of these are much lower than and do not include the conventional value of 0.60. This also implies that risk-neutral default probabilities, and hence risk premia on default probabilities, are underestimated when loss given default is exogenously fixed at the conventional value instead of estimated from the data.

中文翻译：

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在弱识别下估算CDS的违约损失*

本文将信用违约掉期（CDS）的期限结构模型与弱识别稳健方法结合起来，共同估算基础公司的违约概率和给定违约损失。该模型未在全球范围内识别，因为它放弃了在以前的研究中有助于识别的参数时间序列限制，但也难以在数据中进行验证。实证结果表明，样本中半数企业月份的违约损失的信息性（小）置信度集估计得到了评估，其中大多数都远低于常规值0.60，并且不包括传统值0.60。这也意味着风险中性违约概率，因此违约概率风险溢价，