A joint scoring model for peer-to-peer and traditional lending: a bivariate model with copula dependence

Silvia Angela Osmetti, Raffaella Calabrese, Luca Zanin

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We analyse the dependence between defaults in peer-to-peer lending and credit bureaus. To achieve this, we propose a new flexible bivariate regression model that is suitable for binary imbalanced samples. We use different copula functions to model the dependence structure between defaults in the two credit markets. We implement the model in the R package BivGEV and we explore the empirical properties of the proposed fitting procedure by a Monte Carlo study. The application of this proposal to a comprehensive data set provided by Lending Club shows a significant level of dependence between the defaults in peer-to-peer and credit bureaus. Finally, we find that our model outperforms the bivariate probit and univariate logit models in predicting peer-to-peer default, in estimating the value at risk and the expected shortfall.
Original languageEnglish
Pages (from-to)1163-1188
Number of pages26
JournalJOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY
Volume182
DOIs
Publication statusPublished - 2019

Keywords

  • Binary imbalanced samples
  • Copula-based model
  • Credit bureau
  • Generalized extreme value regression model
  • Peer-to-peer lending

Fingerprint

Dive into the research topics of 'A joint scoring model for peer-to-peer and traditional lending: a bivariate model with copula dependence'. Together they form a unique fingerprint.

Cite this