Deep learning-based least squares forward-backward stochastic differential equation solver for high-dimensional derivative pricing
将深度学习求解器与最小二乘蒙特卡洛方法结合，提出一种新的正倒向随机微分方程求解器，用于高维衍生品定价，能准确为可赎回收益票据等复杂早期可执行衍生品定价。
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivative pricing problems by combining a deep learning solver with a least squares regression technique widely used in the least squares Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the accuracy of our least squares backward deep neural network solver and its capability to produce accurate prices for complex early exercisable derivatives, such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an accurate means for pricing high-dimensional derivatives with early exercise features.