Deep hedging strays when volatility gets rough – study

In the most realistic simulations, data-driven approach fared 30% worse than conventional hedging

Volatility hits algos montage

For derivatives hedgers preparing to throw out their Black-Scholes models in favour of new data-driven algorithms, a recent study may give pause for thought.

Banks such as JP Morgan have experimented in recent years with so-called deep hedging, in which machine learning systems are trained to hedge complex derivatives books. It’s an alternative to the use of conventional parametric models like Black-Scholes, which calculate options hedges based on variables such as the price of the underlying

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