Industry Distress and Default Recovery Rates: The Unconditional Quantile Regression Approach
43 Pages Posted: 30 Oct 2019 Last revised: 28 Sep 2020
Date Written: September 26, 2020
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: Suggested Citation