Date | Dec 02, 2022 |
---|---|

Speaker | 이기섭 |

Dept. | 퍼듀대학교 통계학과 |

Room | 27-325 |

Time | 16:00-17:00 |

We investigate option hedging in an incomplete market with a reinforcement learning algorithm called double deep Q-network (DDQN). The agent of DDQN learns the optimal policy that generates replicating portfolios without prior knowledge of the stochastic representation of an underlying asset price process. First, we interpret a mean-variance approach in quadratic hedging in a reinforcement learning framework. This study includes three simulation studies for different underlying asset price processes: geometric Brownian motion (GBM), Heston, and GBM with compound Poisson jumps. For each study, a DDQN agent learns the optimal policy, and we compare the algorithm performance with delta hedging. Second, we discuss limitations that stem from the structure of reinforcement learning in finance.

TEL 02-880-5857,6530,6531 / FAX 02-887-4694