Predicting recovery rates using logistic quantile regression with bounded outcomes
提出逻辑分位数回归方法，利用穆迪终极回收数据库，通过误差最小化分位点选择，在滚动窗口预测中比传统方法更准确稳健地预测回收率。
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.