Pricing and Inference with Mixtures of Conditionally Normal Processes

We consider the problems of derivative pricing and inference when the stochastic discount factor has an exponentialaffineform and the geometric return of the underlying asset has a dynamics characterized by a mixture of conditionallyNormal processes. We consider both the static case in which the underlying process is a white noise distributed as amixture of Gaussian distributions (including extreme risks and jump diffusions) and the dynamic case in which theunderlying process is conditionally distributed as a mixture of Gaussian laws. Semi-parametric, non parametric andSwitching Regime situations are also considered. In all cases, the risk-neutral processes and explicit pricing formulasare obtained.