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Activity Number: 52
Type: Invited
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #318031
Title: Sequential Monte Carlo with Parameter Learning for Long-Memory Processes
Author(s): Konstantinos Spiliopoulos* and Alexandra Chronopoulou
Companies: Boston University and University of Illinois Urbana-Champaign
Keywords:
Abstract:

We consider a state-space model that is specified up to an unknown vector of parameters and in which the unobserved state process exhibits long-memory. Our goal is to estimate both the state process and the parameter vector. For this, we propose a sequential Monte Carlo method that is based on smoothing of the sample points of model parameters. We establish a central limit theorem for the state and parameter filter and we illustrate our results with a simulation study. Finally, we apply our approach to S& P 500 data in the context of a stochastic volatility model with long memory.


Authors who are presenting talks have a * after their name.

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