  Efficiently pricing multi-asset options is a challenging problem in
quantitative finance. When the characteristic function is available,
Fourier-based methods are competitive compared to alternative techniques
because the integrand in the frequency space often has a higher regularity than
that in the physical space. However, when designing a numerical quadrature
method for most Fourier pricing approaches, two key aspects affecting the
numerical complexity should be carefully considered: (i) the choice of damping
parameters that ensure integrability and control the regularity class of the
integrand and (ii) the effective treatment of high dimensionality. We propose
an efficient numerical method for pricing European multi-asset options based on
two complementary ideas to address these challenges. First, we smooth the
Fourier integrand via an optimized choice of the damping parameters based on a
proposed optimization rule. Second, we employ sparsification and
dimension-adaptivity techniques to accelerate the convergence of the quadrature
in high dimensions. The extensive numerical study on basket and rainbow options
under the multivariate geometric Brownian motion and some L\'evy models
demonstrates the advantages of adaptivity and the damping rule on the numerical
complexity of quadrature methods. Moreover, for the tested two-asset examples,
the proposed approach outperforms the COS method in terms of computational
time. Finally, we show significant speed-up compared to the Monte Carlo method
for up to six dimensions.
