A fast Monte Carlo scheme for additive processes and option pricing

In this paper, we present a very fast Monte Carlo scheme for additive processes: the computational time is of same order magnitude standard algorithms Brownian motions. We analyze in detail numerical error sources and propose technique that reduces two major error. also compare our results with benchmark method: jump simulation Gaussian approximation. show an application to normal tempered stable processes, class processes calibrates ``exactly" implied volatility surface.Numerical are relevant. This algorithm accurate tool pricing path-dependent discretely-monitoring options errors one bp or below.