Ensemble learning for portfolio valuation and risk management
提出一种基于回归树的集成学习方法，从有限样本的累积现金流中学习衍生品组合的动态价值过程，适用于高维问题，数值实验在12维和36维上表现良好。
We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given in closed form. The method is fast and accurate, and scales well with sample size and path space dimension. It can also be applied to Bermudan options. Numerical experiments show good results for examples in dimensions 12 and 36.