论文标题
大规模帐户级蒙特卡洛债务恢复模型的有效预测和不确定性量化
Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery
论文作者
论文摘要
我们考虑了从管理中不良的无抵押消费者贷款的大量投资组合中预测债务收回的问题。行业中的最新状态是使用随机流程来大致根据几个协变量（包括信用评分和付款历史记录）模拟单个客户的支付行为。对这些随机过程的蒙特卡洛模拟可以预测违约债务投资组合的可能收益以及不确定性的量化。 尽管个人级别的模型相对简单，但由于大量帐户的数量，在投资组合级别进行仿真还是具有挑战性的。这些帐户也是异质的，收集方差的值范围很大。 我们旨在解决两个主要问题：在模拟中有效分配计算资源，以尽可能准确地估算可能的收集，并量化预测中的不确定性。我们表明，在某些条件下，可以通过对个体帐户差异的粗制估计量来构建人口级别差异的稳健估计器。提出的方法是通过应用于模型的应用，该模型与实践中使用的方法共享关键功能。
We consider the problem of forecasting debt recovery from large portfolios of non-performing unsecured consumer loans under management. The state of the art in industry is to use stochastic processes to approximately model payment behaviour of individual customers based on several covariates, including credit scores and payment history. Monte Carlo simulation of these stochastic processes can enable forecasting of the possible returns from portfolios of defaulted debt, and the quantification of uncertainty. Despite the fact that the individual-level models are relatively simple, it is challenging to carry out simulations at the portfolio level because of the very large number of accounts. The accounts are also heterogeneous, with a broad range of values for the collection variances. We aim to solve two main problems: efficient allocation of computational resources in the simulations to estimate the likely collections as precisely as possible, and quantification of the uncertainty in the forecasts. We show that under certain conditions, robust estimators of population-level variance can be constructed by summing over coarse unbiased estimators of the variance of individual accounts. The proposed methods are demonstrated through application to a model which shares key features with those that are used in practice.
