Predicting recovery rates using logistic quantile regression with bounded outcomes
Predicting recovery rates using logistic quantile regression with bounded outcomes
复制标题
使用具有有限结果的逻辑分位数回归预测恢复率
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Chu
中科院分区：
文献类型：
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作者：
Jhao;R. Hwang;C. Chu
采用Logistic分位数回归（LQR）研究回收率。它是用单调变换来发展的。使用穆迪的最终恢复数据库，我们显示了估计样本的不同分区中的恢复率具有不同的分布，因此为了预测恢复率，为LQR确定了每个分区上的误差最小化分位数点。使用扩展滚动窗口方法，实证结果证实，具有最小误差分位数点的LQR在产生更准确的预测回收率的意义上，比其竞争方案具有更好和更稳健的样本外性能。因此，LQR是研究回收率的有用替代方法。
Logistic quantile regression (LQR) is used for studying recovery rates. It is developed using monotone transformations. Using Moody’s Ultimate Recovery Database, we show that the recovery rates in different partitions of the estimation sample have different distributions, and thus for predicting recovery rates, an error-minimizing quantile point over each of those partitions is determined for LQR. Using an expanding rolling window approach, the empirical results confirm that LQR with the error-minimizing quantile point has better and more robust out-of-sample performance than its competing alternatives, in the sense of yielding more accurate predicted recovery rates. Thus, LQR is a useful alternative for studying recovery rates.
影响因子：
2.7
作者：
Bondell, Howard D.;Reich, Brian J.;Wang, Huixia
通讯作者：
Wang, Huixia