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Algorithmic Differentiation and the Buy-side
By Nik Venema | May 10, 2016

FINCAD continued its video series with John Hull, PhD and Alan White, PhD, interviewing them on how adjoint algorithmic differentiation (AAD) is helping the buy-side to measure and manage risk more effectively. When compared to traditional risk methods known as “bumping,” AAD offers firms the ability to solve complex pricing and risk challenges quickly and accurately.

“Adjoint algorithmic differentiation is a new, exciting idea in derivative markets,” commented Hull. “From the point of view of FINCAD, it’s very close to what the company does because it’s akin to how one writes code. That is, what AAD involves is inserting instructions typically between every line of code in order to calculate partial derivatives much faster than they were ever calculated before.” He continued, “The interesting thing about AAD is that you can’t just write a macro and be done with it. You actually need an intelligent human being going through the code and writing the necessary instructions to calculate these partial derivatives. So, it’s a fairly labor-intensive business."

"I’m pleased that FINCAD is right at the forefront of AAD because I think it’s the way of the future,” explained Hull.

White weighed in, “AAD is very consistent with the evolution of the derivatives market, which has moved from exotic products to exotic risk management. There are now incredibly computationally-intensive procedures firms have to use to calculate the appropriate risk measures.” White elaborated, “One might say, regulators are pushing institutions in the direction of AAD. It is essentially impossible to measure risk of OTC derivatives without using Monte Carlo simulation, and so anything that can speed up computations is very helpful.”  

To hear more of Hull and White’s insights on this topic, including the connection between AAD and regulation, check out the video