Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures

2021-11-15 · Hans Buehler, Phillip Murray, Mikko S. Pakkanen, Ben Wood

We present a machine learning approach for finding minimal equivalent martingale measures for markets simulators of tradable instruments, e.g. for a spot price and options written on the same underlying. We extend our results to markets with frictions, in which case we find "near-martingale measures" under which the prices of hedging instruments are martingales within their bid/ask spread. By removing the drift, we are then able to learn using Deep Hedging a "clean" hedge for an exotic payoff which is not polluted by the trading strategy trying to make money from statistical arbitrage opportunities. We correspondingly highlight the robustness of this hedge vs estimation error of the original market simulator. We discuss applications to two market simulators.

📄 PDF Abstract BibTeX arXiv:2111.07844

Code (0)

등록된 구현이 없습니다.

Hedging, arbitrage and optimality with superlinear frictions

2015-06-19

In a continuous-time model with multiple assets described by c\`{a}dl\`{a}g processes, this paper characterizes superhedging prices, absence of arbitrage, and utility maximizing strategies, under general frictions that m…

On the quasi-sure superhedging duality with frictions

2019-09-18

We prove the superhedging duality for a discrete-time financial market with proportional transaction costs under model uncertainty. Frictions are modeled through solvency cones as in the original model of [Kabanov, Y., H…

Math

Deep Bellman Hedging

2022-07-03 · Hans Buehler, Phillip Murray, Ben Wood

We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter derivatives using purely historic data. The…

reinforcement-learning Reinforcement Learning (RL)

Deep Learning Algorithms for Hedging with Frictions

2021-11-02 · Xiaofei Shi, Daran Xu, Zhanhao Zhang

This work studies the deep learning-based numerical algorithms for optimal hedging problems in markets with general convex transaction costs on the trading rates, focusing on their scalability of trading time horizon. Ba…

Deep Learning Time Series Analysis Transfer Learning

Reinforcement Learning for Option Hedging: Static Implied-Volatility Fit versus Shortfall-Aware Performance

2026-01-05 · Ziheng Chen, Minxuan Hu, Jiayu Yi, Wenxi Sun arxiv

We extend the Q-learner in Black-Scholes (QLBS) framework by incorporating risk aversion and trading costs, and propose a novel Replication Learning of Option Pricing (RLOP) approach. Both methods are fully compatible wi…

Reinforcement Learning