Brownian meanders, importance sampling and unbiased simulation of diffusion extremes

Computing expected values of functions involving extreme values of diffusion processes can find many applications in financial engineering. Conventional discretization simulation schemes often converge slowly. We propose a Wiener-measure-decomposition based approach to construct unbiased Monte Carlo estimators. Combined with the importance sampling technique and the Williams path decomposition of Brownian motion, this approach transforms simulating extreme values of a general diffusion process to simulating two Brownian meanders. Numerical experiments show this estimator performs efficiently for diffusions with and without boundaries.