We consider a Markov process X which is the solution of a stochastic differential equation driven by a Lévy process Z and an independent Wiener process W . Under some regularity conditions, including non-degeneracy of the diffusive and jump components of the process as well as smoothness of the Lévy density of Z outside any neighborhood of the origin, we obtain a small-time secondorder polynomi...

This paper concerns the optimal stopping time problem in a nite horizon of a controlled jump diiusion process. We prove that the value function is continuous and is a viscosity solution of the inte-grodiierential variational inequality arising from the associated dynamic programming. We also establish comparison principles, which yield uniqueness results. Moreover, the viscosity solution approa...

From the spot prices we have to identify the following six parameters: α , μ, σ, Km , γ, Φ. If necessary, a seventh parameter, λ, should be identified from the futures prices. The six parameters mentioned above can be identified using the maximum likelihood method (Ball and Torous, 1983; Lien and Strom, 1999; Clewlow and Strickland, 2000) or the moments method (Lien and Strom, 1999; Deng, 1999)...

We develop Bayesian Markov chain Monte Carlo methods for inferences of continuoustime models with stochastic volatility and infinite-activity Levy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Levy jumps, and (ii) affine jumpdiffusion models fail to adequately approximate the behav...