Wed 01 Sep 2021
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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In summer 2022, the Bank for International Settlements (BIS) published its second consultation paper on the prudential treatment of cryptoasset exposures. The guidelines outlined in the proposed document follow an initial discussion paper released in 2019 and a first consultative document issued in 2021. The complete text is set up as a new standard to […]
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