Market Making of Options via Reinforcement Learning

Market making of options with different maturities and strikes is a challenging problem due to its high dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes. When the arrival of market orders is linearly inverse to the spreads, the optimal policy is normally distributed.