Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

1 code implementation • 10 May 2022 • Jay Cao, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, Jun Yuan

We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset.

Distributional Reinforcement Learning Position +2

Deep Hedging of Derivatives Using Reinforcement Learning

no code implementations • 29 Mar 2021 • Jay Cao, Jacky Chen, John Hull, Zissis Poulos

This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs.

Position reinforcement-learning +1

Deep Learning for Exotic Option Valuation

no code implementations • 22 Mar 2021 • Jay Cao, Jacky Chen, John Hull, Zissis Poulos

We refer to this as the model calibration approach (MCA).

Variational Autoencoders: A Hands-Off Approach to Volatility

no code implementations • 7 Feb 2021 • Maxime Bergeron, Nicholas Fung, John Hull, Zissis Poulos

As a dividend of our first step, the synthetic surfaces produced can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options.