In 2008, a team of quants at JP Morgan set out to automate the hedging of one of the firm’s derivatives portfolios. The effort was quickly abandoned. The hedging strategy—which relied on computing risk sensitivities, known as Greeks—required constant manual adjustments to account for transaction costs and other market frictions, which are not captured in classical quantitative models.
Some years later, they tried again, this time using machine learning. The new system eschews conventional
Rich Newman joins to talk about challenges facing the alternative data space and why open data is becoming increasingly important.Subscribe to Weekly Wrap emails