Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908583509098496 |
|---|---|
| author | Di Wang, Albert Du, Ye |
| author_facet | Di Wang, Albert Du, Ye |
| contents | Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN, to tackle the problem. In particular, our models predict not only the default probability of a loan but also the time when it will default. The experiments demonstrate that both models can significantly reduce the portfolio VaRs at different confidence levels, compared to benchmarks. More interestingly, the low degree of freedom model, DeNN, outperforms DSNN in most scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks Di Wang, Albert Du, Ye Computational Finance Artificial Intelligence Computational Engineering, Finance, and Science Mathematical Finance Portfolio Management Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN, to tackle the problem. In particular, our models predict not only the default probability of a loan but also the time when it will default. The experiments demonstrate that both models can significantly reduce the portfolio VaRs at different confidence levels, compared to benchmarks. More interestingly, the low degree of freedom model, DeNN, outperforms DSNN in most scenarios. |
| title | Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks |
| topic | Computational Finance Artificial Intelligence Computational Engineering, Finance, and Science Mathematical Finance Portfolio Management |
| url | https://arxiv.org/abs/2510.07444 |