Algorithmic differentiation sees huge improvements in speed and accuracy
By Russell Goyder, PhD - Director of
Quantitative Research and Development, Fincad
reform and fierce competition are key factors making it harder
for derivatives dealers to get ahead. As a result, many are
looking at alternative methodologies that can help them gain an
edge in a complex marketplace, while simultaneously easing
One such alternative is
algorithmic differentiation (AD), a mathematical technique that
helps firms with derivatives on their books to rapidly
solve complex pricing and analytics problems. AD enables
staggering improvements in speed and accuracy when compared to
traditional risk methods such as finite differences, also known
Interest in AD is
likely on the rise due to firms’ anticipation of
specific regulations soon to be put into place by the
International Swaps and Derivatives Association (Isda).
Starting in September 2016, with full implementation in March
2017, major derivatives firms will be called on to have a
transparent and standardized approach for initial margin,
called the Standard Initial Margin Model (SIMM). Part of the
Isda requirement includes capturing first order risk
sensitivity with respect to every risk factor on at least a
daily basis for determining margin.
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