Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 46, Issue 8, Pages 6324-6341Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2016.1202275
Keywords
GEV distribution; MCMC; Nonlinear observation; Particle filters; Topix dataset
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Funding
- U.S. Department of Energy, Office of Science, Biological and Environmental Research [DE-SC0010843]
- U.S. Department of Energy (DOE) [DE-SC0010843] Funding Source: U.S. Department of Energy (DOE)
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This article addresses how particle filters compare to MCMC methods for posterior density approximations of a model that allows for a dynamic state with fixed parameters and where the observation equation is nonlinear. This is a problem that was not been well studied in the specialized literature. We prove that these state and parameter estimations can be achieved via particle filter methods without the need of more expensive Forward Filtering Backward Sampling (FFBS) simulation. Estimation of a time-varying extreme value model via the generalized extreme value distribution is considered using these particle filter methods and compared to a MCMC algorithm that involves a variety of Metropolis-Hastings steps. We illustrate and compare the different methodologies with simulated data and some minimum daily stock returns occurring monthly from January 4, 1990 to December 28, 2007 using the Tokyo Stock Price Index (TOPIX).
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