  In this paper, we compare the intrusive proper orthogonal decomposition (POD)
with Galerkin projection and the data-driven dynamic mode decomposition (DMD),
for Heston's option pricing model. The full order model is obtained by
discontinuous Galerkin discretization in space and backward Euler in time.
Numerical results for butterfly spread, European and digital call options
reveal that in general DMD requires more modes than the POD modes for the same
level of accuracy. However, the speed-up factors are much higher for DMD than
POD due to the non-intrusive nature of the DMD.
