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