This paper is devoted to the parameterization of correlations in the Vasicek credit portfolio model. First, we analytically approximate standard errors for value-at-risk and expected shortfall based on the standard errors of intra-cohort correlations. Second, we introduce a novel copula-based maximum likelihood estimator for inter-cohort correlations and derive an analytical expression of the standard errors. Our new approach enhances current methods in terms of both computing time and, most importantly, direct uncertainty quantification. Both contributions can be used to quantify a margin of conservatism, which is required by regulators. Third, we illustrate powerful procedures that reduce the well-known bias of current estimators, showing their favorable properties. Further, an open-source implementation of all estimators in the novel R package AssetCorr is provided and selected estimators are applied to Moody’s Default & Recovery Database.

中文翻译：

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Vasicek信贷组合模型中的参数估计，偏差校正和不确定性量化

本文致力于Vasicek信贷组合模型中相关性的参数化。首先，我们基于组内相关性的标准误差，分析风险值和预期缺口的标准误差。其次，我们针对队列之间的相关性引入了一种新颖的基于copula的最大似然估计器，并得出了标准误差的解析表达式。我们的新方法在计算时间以及最重要的是直接不确定性量化方面都增强了当前方法。两种贡献都可用于量化监管机构要求的保守程度。第三，我们举例说明了有效的程序，这些程序可以减少当前估计量的众所周知的偏差，从而显示出它们的有利属性。进一步，