Predicting recovery rates using logistic quantile regression with bounded outcomes. Issue 5 (3rd May 2016)

• Record Type: Journal Article
• Authors: Siao, Jhao-Siang; Hwang, Ruey-Ching; Chu, Chih-Kang
• Is Part Of: Quantitative finance. Volume 16:Issue 5(2016)
• Page Start: 777
• Page End: 792
• Publication Date: 2016-05-03
• Subjects: Expanding rolling window approach -- Inverse Gaussian regression -- Logistic quantile regression -- Recovery rate; C53 -- G21; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2015.1059952
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : 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.