论文标题
用于高维问题的投资组合定价和风险管理的机器学习方法
A machine learning approach to portfolio pricing and risk management for high-dimensional problems
论文作者
论文摘要
我们根据复制的Martingale提供了一个在离散时间内投资组合风险管理的一般框架。在有限的环境中，从有限样本中学到了这一martingale。该模型了解有效的低维表示所需的功能，从而克服了在高维空间中运行近似值的维度的诅咒。我们根据多项式和神经网络基础显示结果。两者都为幼稚的蒙特卡洛方法和其他现有方法（例如蒙特卡洛和复制投资组合）提供了卓越的结果。
We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios.
