Large deviations for Bayesian estimators in first-order autoregressive processes

In this paper we consider first-order autoregressive processes and we allow either centered Normal or exponential innovations. We prove large deviation principles for posterior distributions on the unknown parameter and, motivated by potential applications in risk theory, we also prove large deviation principles for Bayesian estimators of Lundberg's parameter.