Non- and Semi-Parametric Quantile Models for Recovery Rate

42 Pages Posted: 22 Aug 2016 Last revised: 24 Aug 2016

See all articles by Nithi Sopitpongstorn

Nithi Sopitpongstorn

Monash Business School

Jiti Gao

Monash University - Department of Econometrics & Business Statistics

Param Silvapulle

Monash University - Department of Econometrics & Business Statistics

Xibin Zhang

Monash University

Date Written: August 19, 2016

Abstract

This paper proposes a nonparametric quantile regression (NP-QR) and a partially linear additive QR (PLA-QR) for modelling recovery rates (RR). Using Moody’s Recovery Database, we uncover two novelties of the NP-QR model. First, the local constant estimation of NP-QR model captures the key empirical feature of RR data being bounded in [0,1] interval. Second, the heterogeneity in the impact of borrower characteristics with clarity can be estimated. For example, we show that the way in which the impact of debt cushion on RR depends on the other characteristics, such as collateralisation and the degree of instrument rank of loans, during various economic conditions. By contrast such heterogeneity is reduced to a single dimension in the parametric regression model. Furthermore, accommodating bimodality and heteroscedastic errors, the NP-QR also provides the heterogeneity of these impacts across the various quantiles of the conditional RR distribution. On the other hand, the idiosyncratic marginal effects of borrower characteristics are estimated using the PLA-QR model over the various quantiles of the conditional RR distribution. For example, for loans with very low risk, we find that the RR is less sensitive to the change in debt cushion at the lower quantiles than that at the upper quantile, particularly when the debt cushion is less than 30%, during economic downturns. The findings of this study have implication for lenders designing an optimum treatment rule for borrowers.

Keywords: Loss given default, Credit risk, Borrower characteristics, Nonlinear models, Local constant method

JEL Classification: C14, G21, G28

Suggested Citation

Sopitpongstorn, Nithi and Gao, Jiti and Silvapulle, Param and Zhang, Xibin, Non- and Semi-Parametric Quantile Models for Recovery Rate (August 19, 2016). 29th Australasian Finance and Banking Conference 2016. Available at SSRN: https://ssrn.com/abstract=2826461 or http://dx.doi.org/10.2139/ssrn.2826461

Nithi Sopitpongstorn (Contact Author)

Monash Business School ( email )

Wellington Road
Clayton, Victoria 3168
Australia

Jiti Gao

Monash University - Department of Econometrics & Business Statistics ( email )

900 Dandenong Road
Caulfield East, Victoria 3145
Australia
61399031675 (Phone)
61399032007 (Fax)

HOME PAGE: http://www.jitigao.com

Param Silvapulle

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia
+61 3 990 32237 (Phone)

Xibin Zhang

Monash University ( email )

900 Dandenong Road
Caulfield East, Victoria
Australia
61-3-99032130 (Phone)
61-3-99032007 (Fax)

HOME PAGE: http://users.monash.edu.au/~xzhang/

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