  We consider the problem of static Bayesian inference for partially observed
Levy-process models. We develop a methodology which allows one to infer static
parameters and some states of the process, without a bias from the
time-discretization of the afore-mentioned Levy process. The unbiased method is
exceptionally amenable to parallel implementation and can be computationally
efficient relative to competing approaches. We implement the method on S & P
500 log-return daily data and compare it to some Markov chain Monte Carlo
(MCMC) algorithm.
