Risk - sens tive filtering and smoothing for h olden Markov models *

Sequential Monte Carlo Methods for Filtering and Smoothing in Hidden Markov Models

Assessment of Mortgage Default Risk via Bayesian State Space Models

Managing risk at the aggregate level is crucial for banks and financial institutions as required by the Basel III framework. In this paper, we introduce discrete time Bayesian state space models with Poisson measurements to model aggregate mortgage default rate. We discuss parameter updating, filtering, smoothing, forecasting and estimation using Markov chain Monte Carlo methods. In addition, w...

Switch-Reset Models : Exact and Approximate Inference

Reset models are constrained switching latent Markov models in which the dynamics either continues according to a standard model, or the latent variable is resampled. We consider exact marginal inference in this class of models and their extension, the switch-reset models. A further convenient class of conjugateexponential reset models is also discussed. For a length T time-series, exact filter...

A second-order iterated smoothing algorithm

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximat...

Inference and Filtering for Partially Observed Diffusion Processes via Sequential Monte Carlo

Diffusion processes observed partially or discretely, possibly with observation error, arise when constructing stochastic models in continuous time. The method of Sequential Monte Carlo provides an alternative to Markov Chain Monte Carlo methods, and can be effective in complex models at the cutting edge of scientific research. This paper introduces Sequential Monte Carlo approaches to inferenc...