This presentation is intended to be a short course on inference and filtering in hidden Markov models (HMMs) and state space models. These models comprise a hidden (unobserved) Markov chain (either with discrete or continuous state space) that governs the distribution of an observed stochastic process. An example of a HMM is a regime-switching model for stock returns in which the stock return d...

An iterative stochastic algorithm to perform maximum a posteriori parameter estimation of hidden Markov models is proposed. It makes the most of the statistical model by introducing an artiicial probability model based on an increasing number of the unobserved Markov chain at each iteration. Under minor regularity assumptions, we provide suucient conditions to ensure global convergence of this ...

Mortality dynamics are characterized by changes in mortality regimes. This paper describes a Markov regime switching model which incorporates mortality state switches into mortality dynamics. Using the 1901-2005 US population mortality data, we illustrate that regime switching models can perform better than well-known models in the literature. Furthermore, we extend the Lee-Carter (1992) model ...

GARCH models with Markov-switching regimes are often used for volatility analysis of …nancial time series. Such models imply less persistence in the conditional variance than the standard GARCH model, and potentially provide a signi…cant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this...