Deep hedging: application of deep learning to hedge financial derivatives

The recent breakthrough of data science and deep learning make a model independent approach for hedging possible. This hedging approach known as deep hedging is a robust data-driven method able to consider market frictions as well as trading constraints without using model-computed greeks. This article gives the main theoretical tools to understand the methodology and presents examples of applications in different frameworks (Black-Scholes, Heston and a back-test on real data). The results of those applications show that deep hedging works well with data generated by complex models and can provide a relevant hedging strategy taking into account market constraints.

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