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Title: Particle rolling MCMC with forward and backward block sampling: Conditional sequential Monte Carlo update approach Authors:  Naoki Awaya - University of Tokyo (Japan) [presenting]
Yasuhiro Omori - University of Tokyo (Japan)
Abstract: The objective is to provide a new simulation-based methodology for rolling estimation in state space model from Bayesian approach. This type of estimation requires sampling by simulation-based method from a lot of posteriors if the model does not have so simple form. Repetition of sampling from each posterior by Markov Chain Monte Carlo is not realistic from a viewpoint of computational time so to address this problem a new sampling algorithm based on sequential Monte Carlo is presented. This method is applied to SP 500 data with the realized stochastic volatility with leverage model and how the economic structure which generates the financial data is changed is shown.