Deep hedging

By H. Buhler, Lukas Gonon, Josef Teichmann, Ben Wood. 2018

446 citations recorded by the source corpus.

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show in Section 4 that the set of constrained trading strategies used by our algorithm is large enough to ε-approximate any optimal solution. Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available. We illustrate our approach by an experiment on the S&P500 index and by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard ‘complete-market’ solution.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, background): “…recommend path-dependent actions in dynamic environments, RL is particularly of interest within the context of optimal execution and automated hedging (Bühler et al. 2018; Kolm and Ritter 2019) for example, where actions taken can have an impact on future states of the world (e.g., market impact).”
Universal features of price formation in financial markets: perspectives from deep learning (2018, background): “on and training are orders of magnitude smaller than those encountered in other elds where Big Data analytics have been successfully applied. This is one of the reasons why, except in a few instances [8, 15, 26, 29, 30], large-scale learning methods such as Deep Learning [19] have not been deployed ”
Recent advances in reinforcement learning in finance (2021, methodology): “We refer to the survey Broadie andDetemple (2004) for details about extensions of the BSMmodel, other classical option pricing models, and numerical methods such as the Monte Carlo method.”
Adversarial Deep Reinforcement Learning in Portfolio Management (2018, background): “Hans Buehler presented a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods [16].”