Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outliers

J. Q. Smith and Antonio Santos ()
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J. Q. Smith: Department of Statistics, University of Warwick

No 2005-11, GEMF Working Papers from GEMF, Faculty of Economics, University of Coimbra

Abstract: Particle Filters are now regularly used to obtain the filter distributions associated with state space financial time series. Most commonly used nowadays is the auxiliary particle filter method in conjunction with a first order Taylor expansion of the log-likelihood. We argue in this paper that for series such as stock returns, which exhibit fairly frequent and extreme outliers, filters based on this first order approximation can easily break down. However, an auxiliary particle filter based on the much more rarely used second order approximation appears to perform well in these circumstances. To detach the issue of algorithm design from problems related to model misspecification and parameter estimation, we demonstrate the lack of robustness of the first order approximation and the feasibility of a specific second order approximation using simulated data.

Keywords: Bayesian inference; Importance sampling; Particle filter; State space model; Stochastic volatility. (search for similar items in EconPapers)
Pages: 36 pages
Date: 2005
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Citations: View citations in EconPapers (1)

Published in Journal of Business and Economic Statistics, 24(3): 329-337, 2006.

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https://estudogeral.uc.pt/bitstream/10316/11758/1/ ... 20Approximations.pdf (application/pdf)

Related works:
Journal Article: Second-Order Filter Distribution Approximations for Financial Time Series With Extreme Outliers (2006)
Working Paper: Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier (2003)
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