论文标题
深度套期保值：持续的加固学习，用于跨多种风险规避的一般投资组合对冲
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions
论文作者
论文摘要
我们提出了一种方法，用于寻找任意初始投资组合和市场国家的最佳对冲政策。我们开发了一种新型的参与者批评算法，用于解决一般的规避风险随机控制问题，并使用它同时学习跨多种风险规避水平的对冲策略。我们在随机波动性环境中以数值示例来证明该方法的有效性。
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.
