Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks
Albert Di Wang and Ye Du
风险管理是点对点贷款的突出问题。 投资者可以通过多样化而不是将所有资金放在一笔贷款上来自然减少他的风险敞口。 在这种情况下,投资者可能希望尽量减少其贷款组合的高风险价值(VaR)或有条件价值风险(CVaR)。 我们提出了一个低自由度的深度神经网络模型DeNN,以及一个高度的自由模型DSNN,以解决这个问题。 特别是,我们的模型不仅预测了贷款的违约概率,而且还预测了违约的时间。 实验表明,与基准相比,这两种模型都可以显着降低不同置信水平的投资组合VaR。 更有趣的是,低自由度模型DeNN在大多数场景中都优于DSNN。
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 ...