Time Deep Gradient Flow Method for pricing American options
Jasper Rou
本研究探索了基于神经网络的方法，用于在BlackScholes和Heston模型下对多维美式看跌期权进行定价，维度扩展至五维。我们重点关注两种方法：Time Deep Gradient Flow(TDGF)方法和Deep Galerkin Method(DGM)。我们将TDGF方法扩展用于处理美式期权固有的自由边界偏微分方程。在训练过程中，我们精心设计了采样策略以提高性能。TDGF和DGM都实现了高精度，同时在计算速度上优于传统的蒙特卡洛方法。特别是，TDGF在训练过程中往往比DGM更快。
In this research, we explore neural network-based methods for pricing multidimensional American put options under the BlackScholes and Heston model, extending up to five dimensions. We focus on two approaches: the Time Deep Gradient Flow (TDGF) method and the Deep Galerkin Method (DGM). We extend the TDGF method to handle the free-boundary partial differential equation inherent in American options. We carefully design the sampling strategy during training to enhance performance. Both TDGF and DG...