Low-bias simulation scheme for the Heston model by Inverse Gaussian approximation

Fast and accurate sampling of conditional time-integrated variance in the Heston model is an important and challenging problem. We proved that this very complicated distribution converges to the moment-matched Inverse Gaussian distribution as the time interval goes to infinity. Leveraging on this theoretical result, we develop an efficient and accurate Inverse Gaussian approximation to sample conditional time-integrated variance. Numerical results demonstrate that our scheme compares favourably with state-of-the-art methods in accuracy given the same computational time for moderately path-dependent options.