Simultaneous upper and lower bounds of American-style option prices with hedging via neural networks
提出两种神经网络方法同步求解美式期权价格的上下界，避免嵌套蒙特卡洛，降低计算复杂度，适用于高维频繁行权的百慕大期权，并附带对冲策略。
In this paper, we introduce two novel methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds of the option price, and the second one accomplishes the same goal with one global network. The avoidance of extra simulations and the use of neural networks significantly reduce the computational complexity and allow us to price Bermudan options with frequent exercise opportunities in high dimensions, as illustrated by the provided numerical experiments. As a by-product, these methods also derive a hedging strategy for the option, which can also be used as a control variate for variance reduction.