Deep Quadratic Hedging
提出一种基于深度学习的计算方法，用于高维不完全市场中的二次对冲，包括均值方差对冲和局部风险最小化，通过递归求解倒向随机微分方程来获得最优对冲策略路径，克服了维度诅咒。
We propose a novel computational procedure for quadratic hedging in high-dimensional incomplete markets, covering mean-variance hedging and local risk minimization. Starting from the observation that both quadratic approaches can be treated from the point of view of backward stochastic differential equations (BSDEs), we (recursively) apply a deep learning-based BSDE solver to compute the entire optimal hedging strategies paths. This allows us to overcome the curse of dimensionality, extending the scope of applicability of quadratic hedging in high dimension. We test our approach with a classic Heston model and with a multiasset and multifactor generalization thereof, showing that this leads to high levels of accuracy.