Downturn LGD Modeling Using Quantile Regression

Posted: 28 Feb 2018

Date Written: 2017


Literature on Losses Given Default (LGD) usually focuses on mean predictions, even though losses are extremely skewed and bimodal. This paper proposes a Quantile Regression (QR) approach to get a comprehensive view on the entire probability distribution of losses. The method allows new insights on covariate effects over the whole LGD spectrum. In particular, middle quantiles are explainable by observable covariates while tail events, e.g., extremely high LGDs, seem to be rather driven by unobservable random events. A comparison of the QR approach with several alternatives from recent literature reveals advantages when evaluating downturn and unexpected credit losses. In addition, we identify limitations of classical mean prediction comparisons and propose alternative goodness of fit measures for the validation of forecasts for the entire LGD distribution.

Keywords: Loss given default, Downturn, Quantile regression, Recovery, Validation

JEL Classification: G20, G28, C51

Suggested Citation

Krueger, Steffen and Roesch, Daniel, Downturn LGD Modeling Using Quantile Regression (2017). Journal of Banking and Finance, Vol. 79, 2017, Available at SSRN:

Steffen Krueger

Independent ( email )

Daniel Roesch (Contact Author)

University of Regensburg ( email )

Chair of Statistics and Risk Management
Faculty of Business, Economics and BIS
Regensburg, 93040


Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics