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Abstract Details
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Activity Number:
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517
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306527 |
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Title:
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Robust Filtering
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Author(s):
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Veronika Czellar*+ and Laurent Calvet
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Companies:
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HEC Paris and HEC Paris
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Address:
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1 Rue De La Liberation, Jouy En Josas 78351, , France
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Keywords:
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Robust statistics ;
Particle filter ;
Misspecification ;
Financial volatility
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Abstract:
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Filtering methods are known to be highly sensitive to small misspecifications of the underlying model. This paper illustrates that the methodology of robust statistics can be adapted to sequential filtering problems. We introduce an influence function that quantifies the sensitivity of the state distribution with respect to new data. We show that the influence function of a standard filter is unbounded, even in the simplest cases, and propose a particle filter with a bounded influence function. We verify that this new, robust filter provides accurate state space and parameter inference in the presence of model misspecifications. We illustrate its accuracy on models of financial volatility.
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