AI Case Study

JP Morgan to cut hedging costs for commodity derivatives by up to 80% using machine learning

JP Morgan has leveraged machine learning to automate the hedging of equity options. Their system uses machine learning to reveal statistical relationships between variables in large datasets of historical market data. The bank's staff estimate that using the technique they could cut hedging costs for specific commodity derivatives by up to 80%.

Industry

Financial Services

Banking

Project Overview

"JP Morgan is using machine learning to automate the hedging of some equity options, a move that one quant calls a “game-changer”. The bank started using machine learning to hedge a portion of its index vanilla flow book last year. Since then, it has been able to hedge its exposures faster, and quote higher volumes as a result. “The real advantage is we are able to increase volumes quoted – because we are faster,” says Hans Buehler, global head of equities analytics, automation and optimisation at JP Morgan in London. “If you have to manually manage this, you have to divert somebody’s time and sit them down to focus on it.

One senior quant calls JP Morgan’s approach a “base-level rethink” of hedging, which he says will benefit illiquid markets in particular. He estimates the technique has the potential to cut hedging costs for certain commodity derivatives by as much as 80%. “There are lots of places in the market where there is either illiquidity in the hedging instruments you have or large transaction costs or products that have risks that are unhedgeable,” says Mark Higgins, chief operating officer and co-founder of Beacon Platform in New York and co-head of JP Morgan’s quantitative research team until 2014. “In those places, it will be a real paradigm change in how people can approach optimal hedging,” he says.

JP Morgan has used similar machine learning models to provide optimal execution for clients in cash equities for nearly two years. The bank plans to roll out comparable technology for hedging single stocks, baskets and light exotics next year. Quants are embracing so-called model-free machine learning techniques, such as complex statistical regression, to solve sticky problems. These approaches attempt to identify patterns in data without necessarily trying to explain the results within an existing model framework. More advanced methodologies are also in the works."

Reported Results

Results not yet available

Technology

JP Morgan’s new hedging programme uses complex statistical regression, a type of machine learning that tries to find statistical relationships between variables by trawling through large amounts of data. The technique relies on historical market data rather than risk sensitivities – or Greeks – to estimate hedging costs, a dramatic shift from the popular Black-Scholes model.

Function

Operations

Trading

Background

"“Black-Scholes Greeks were very useful in the 1980s because we didn’t have a ton of data and we didn’t have a ton of computing power. So this was an approximation that worked very well for a long time. Today, we have much more data. If you revisit the problem of hedging derivatives now, I don’t think you would sit there and build the Black-Scholes model,” says Buehler [global head of equities analytics, automation and optimisation at JP Morgan in London].

Machine learning models consider many more variables and data points when making hedging decisions, and can generate more accurate hedges at greater speeds, he says."

Benefits

Data