Abstract / Description of output
We analyse the dependence between defaults in peer-to-peer (P2P) lending and credit bureaus.To achieve this aim, we propose a new flexible bivariate regression model 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 dataset provided by Lending Club shows a significant level of dependence between the defaults in P2P and credit bureaus. Finally, we find that our model outperforms the bivariate probit and univariate log it in predicting P2P default, in estimating the Value at Risk and the expected Shortfall.
Original language | English |
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Pages (from-to) | 1163-1188 |
Number of pages | 26 |
Journal | Journal of the Royal Statistical Society: Statistics in Society Series A |
Volume | 182 |
Issue number | 4 |
Early online date | 7 Oct 2019 |
DOIs | |
Publication status | E-pub ahead of print - 7 Oct 2019 |
Keywords / Materials (for Non-textual outputs)
- binary imbalanced samples
- copula-based model
- credit bureau
- generalized extreme value regression model
- peer-to-peer lending
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Raffaella Calabrese
- Business School - Personal Chair of Data Science
- Management Science and Business Economics
- Credit Research Centre
- Management Science
- Edinburgh Centre for Financial Innovations
Person: Academic: Research Active