JP Morgan quants are building deep hedging 2.0

New model uses Bellman technique to learn general derivatives hedging strategies

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JP Morgan quants are working on the next iteration of the firm’s machine learning hedging engine – a version that learns to hedge any book of options rather than just one book at a time.

The bank’s so-called deep hedging engine uses machine learning to work out how to hedge derivatives books from raw data, factoring in real-world market frictions such as transaction costs. Quants have described it as the most exciting research in derivatives pricing and risk management.

But the current engine

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