Risk and return prediction for pricing portfolios of non-performing consumer credit

TL;DR

This paper introduces a novel bottom-up system for risk analysis and pricing of non-performing consumer credit portfolios, utilizing advanced statistical models to accurately predict repayment distributions.

Contribution

It is the first to implement a bottom-up approach combining quantile regression and copula models for consumer credit portfolio risk assessment.

Findings

Method successfully models individual loan repayment rates.

System effectively predicts portfolio-level risk and pricing.

Validated on large datasets with practical business applicability.

Abstract

We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the need for trading portfolios formed by overdue loans as a manner of risk transferring. However, the problem is nontrivial technically and related research is absent. We tackle the challenge by building a bottom-up architecture, in which we model the distribution of every single loan's repayment rate, followed by modeling the distribution of the portfolio's overall repayment rate. To address the technical issues encountered, we adopt the approaches of simultaneous quantile regression, R-copula, and Gaussian one-factor copula model. To our best knowledge, this is the first study that successfully adopts a bottom-up system for analyzing credit portfolio risks of consumer loans. We conduct experiments on a vast amount of data and prove that our methodology can be applied successfully in real business tasks.