We propose a new procedure to predict the loss given default (LGD) distribution. Studies find empirical evidence that LGD values have a high concentration at the endpoint 0. Thus, we first use a logistic regression to determine the probability that the LGD value of a defaulted debt equals zero. Further, studies find empirical evidence that positive LGD values have a low concentration at the endpoint 1 and a bimodal distribution on the interval (0,1). Therefore, we use a right-tailed censored beta-mixture regression to model the distribution of positive LGD data. To implement the proposed procedure, we collect 5554 defaulted debts from Moody’s Default and Recovery Database and apply an expectation–maximization algorithm to estimate the LGD distribution. Using each of the k -fold cross-validation technique and the expanding rolling window approach, our empirical results confirm that the new procedure has better and more robust out-of-sample performance than its alternatives because it yields more accurate predictions of the LGD distribution.

中文翻译：

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使用允许概率质量和双峰性的零膨胀截尾 Beta 混合回归预测给定默认分布的损失

我们提出了一种新的程序来预测给定默认损失 (LGD) 分布。研究发现经验证据表明 LGD 值在端点 0 处高度集中。因此，我们首先使用逻辑回归来确定违约债务的 LGD 值为零的概率。此外，研究发现经验证据表明正 LGD 值在端点 1 处具有低浓度，并且在区间 (0,1) 上呈双峰分布。因此，我们使用右尾删失 beta 混合回归来模拟正 LGD 数据的分布。为实施建议的程序，我们从穆迪违约和恢复数据库收集了 5554 笔违约债务，并应用期望最大化算法来估计 LGD 分布。使用 k 折交叉验证技术和扩展滚动窗口方法中的每一种，