Podcast: Hans Buehler on the data science behind deep hedging

Machine learning systems are only as smart as the data used to train them. Train a neural network on equity market data from a bull market, and its simulation will undoubtedly display an upward drift.

“If you ask a machine to build a trading strategy for an empty portfolio . . . it will start selling puts and buying delta,” says Hans Buehler, global head of equities analytics, automation and optimisation at JP Morgan, and Risk.net’s quant of the year for 2022.

Of course, such a strategy would have produced significant losses this year, when US equity indexes suffered their first correction since the beginning of the Covid-19 pandemic. Removing the drift in financial data series is therefore a prerequisite to training trading strategies, especially hedging algorithms. In a recent paper, Buehler and his co-authors Phillip Murray, Mikko Pakkanen and Ben Wood describe a technique for doing exactly that.

In this episode of Quantcast, Buehler explains the importance of removing drift in financial data, and discusses the general features and uses of deep hedging, as well as his future research projects. He also reveals how the origins of deep hedging can be traced back to his time as a PhD student at the Technical University of Berlin.

Buehler’s research has come a long way since then. JP Morgan already uses a version of deep hedging to price a large chunk of its Eurostoxx vanilla index options book and an increasing portion of S&P 500 options trades. Deep hedging is now being tested for cliquet options and the bank plans to expand its use to single stocks by year-end. Autocallable products, for which deep hedging was originally designed, are likely to be the next product to be priced this way, Buehler says.

Towards the end of the podcast, Buehler shares his ambition to introduce modern machine learning and AI-based methods to JP Morgan’s sales team, and further strengthen the bank’s electronic trading capabilities.

To hear the full interview, listen in the player above, or download. Future podcasts in our Quantcast series will be uploaded to Risk.net. You can also visit the main page here to access all tracks, or go to the iTunes store or Google Podcasts to listen and subscribe.

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Index

00:00 What is deep hedging and what is it used for?

06:30 The importance of removing drift from training datasets

09:03 Application of the de-trending technique

11:25 The origins of deep hedging

15:15 Future research projects