Local logit regression for loan recovery rate

Nithi Sopitpongstorn, Param Silvapulle, Jiti Gao and Jean-Pierre Fenech ()

Journal of Banking & Finance, 2021, vol. 126, issue C

Abstract: This is the first paper to propose a flexible local logit regression for defaulted loan recoveries that lie in [0,1]. Via a simulation study, we demonstrate that the proposed model is robust to nonlinearity, and non-normality of errors. Applied to Moody’s dataset, the local logit model uncovers the intrinsic nonlinear relationship between loan recoveries and covariates, which include loan/borrower characteristics and economic conditions. We exploit the empirical features of the local logit model to improve the specification of the standard regression for the fractional response variable (RFRV) model, which we refer to as the calibrated-RFRV model. The estimation of the calibrated-RFRV model is more straightforward and faster than the local logit model. The overall out-of-sample predictive performance of the calibrated-RFRV is superior to the local logit, RFRV, neural network (NN), regression tree (RT) and Inverse Gaussian (IG) models. The local logit model outperforms others in quantile forecasting, showing the attractiveness of this model for estimating tail risks, the accurate estimation of which is beneficial to risk managers.

Keywords: Loss given default; recovery prediction; nonlinearity; kernel estimation; defaulted loan (search for similar items in EconPapers)
JEL-codes: C14 C53 G02 G32 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378426621000510
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:126:y:2021:i:c:s0378426621000510

DOI: 10.1016/j.jbankfin.2021.106093

Access Statistics for this article

Journal of Banking & Finance is currently edited by Ike Mathur

More articles in Journal of Banking & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().