Second Order Filter Distribution Approximations for Financial Time Series
with Extreme Outlier
(Publicado em Journal of Business and Economic Statistics 24(3): 329-337(9), 2006)
J. Q. Smith
Department of Statistics, University of Warwick
António A. F. Santos
Faculty of Economics, University of Coimbra
Particle Filters are now regularly used to obtain the filter distributions associated with state space financial time series. The method 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 return, which exhibit fairly frequent and extreme outliers, filters based on this first order approximation can easily break down. However, the auxiliary particle filter based on the much more rarely used second order approximation appears to perform well in these circumstances. We demonstrate our results with a typical stock market series.
Keywords: Particle filters; Second order approximations; State space models; Stochastic volatility.