We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-averse stochastic control problems and use it to learn hedging strategies across multiple risk aversion levels simultaneously. We demonstrate the effectiveness of the approach with a numerical example in a stochastic volatility environment.

翻译：我们为任意的初始投资组合和市场状态提供了一种寻找最佳套期保值政策的方法。 我们开发了一种新颖的行为体-批评算法,以解决普遍风险反常的随机控制问题,并同时用于学习多重风险反常水平的套期保值战略。 我们用随机波动环境中的数字示例展示了这种方法的有效性。