Industry Distress and Default Recovery Rates: The Unconditional Quantile Regression Approach

43 Pages Posted: 30 Oct 2019

See all articles by Hui-Ching Chuang

Hui-Ching Chuang

Yuan Ze University - College of Management

Jau‐er Chen

National Taiwan University

Date Written: October 21, 2019

Abstract

In this study, we estimate the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR) proposed in Firpo, Fortin, and Lemieux (2009). The UQR provides better interpretative and thus policy-relevant information on the marginal effect of the covariates than the conditional quantile regression (CQR, Koenker and Bassett, 1978). To deal with a broad set of macroeconomic and industry variables, we use the LASSO-based double selection to identify the effects of industry distress and select variables.Our sample consists of 5,334 debt and loan instruments in Moody's Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. In contrast to the CQR, the UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires.

Keywords: LGD, LASSO, treatment effect, recentered influence function, double selection

JEL Classification: C21, C58, G21

Suggested Citation

Chuang, Hui-Ching and Chen, Jau‐er, Industry Distress and Default Recovery Rates: The Unconditional Quantile Regression Approach (October 21, 2019). Available at SSRN: https://ssrn.com/abstract=3473161

Hui-Ching Chuang (Contact Author)

Yuan Ze University - College of Management ( email )

135, Yuan-Tung Rd.
Taoyuan, 320
Taiwan
886-972-735-021 (Phone)

Jau‐er Chen

National Taiwan University

1 Sec. 4, Roosevelt Road
Taipei 106, 106
Taiwan

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