  We build on the work in Fackler and King 1990, and propose a more general
calibration model for implied risk neutral densities. Our model allows for the
joint calibration of a set of densities at different maturities and dates
through a Bayesian dynamic Beta Markov Random Field. Our approach allows for
possible time dependence between densities with the same maturity, and for
dependence across maturities at the same point in time. This approach to the
problem encompasses model flexibility, parameter parsimony and, more
importantly, information pooling across densities.
