Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
![Risk.net](https://www.risk.net/sites/default/files/styles/print_logo/public/2018-09/print-logo.png?itok=1TpHrpuP)
Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default
Need to know
- We propose two new families of differentiable functions for modelling the CAP.
- We compare four different calibration approaches on a real-world data set.
- One of the new one-parametric families outperforms the linear logistic regression.
- We derive uncertainties of PDs stemming from the discriminatory power’s dispersion.
Abstract
The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase of credit risk models, however, the standard calibration approach is currently under scrutiny again. In particular, the assumptions behind the linear logistic regression provide critics with a target. The previous literature has converted the calibration problem into a regression task without any loss of generality. In this paper, we draw on recent academic results in order to suggest two new single-parameter families of differentiable functions as candidates for this regression. The derivation of these two families of differentiable functions is based on the maximum entropy principle, and thus it relies on a minimum number of assumptions. We compare the performance of four calibration approaches on a realworld data set and find that one of the new single-parameter families outperforms the linear logistic regression. Further, we develop an approach to quantify that part of the general estimation error of probabilities of default which stems from the statistical dispersion of the discriminatory power.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net