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Particle filter estimation of duration models
Miguel Belmonte, Omiros Papaspiliopoulos and Mike Pitt
Computational Statistics and Data Analysis
2008.
Abstractn this paper we model financial durations by discrete and continuous-time point processes in
state-space form (SSF). We illustrate our analysis on a duration dataset analysed in Engle
(2000). For estimation of intensity and static parameters, we resort to particle filters. We
compare estimates delivered by simulation-based filters, with methodology suggested in
Engle (2000) and Bauwens and Veredas (2004).
We conclude that the smooth particle filter (SPF) of Pitt (2002) is an efficient method
for off-line parameter estimation and on-line filtering of univariate SSF duration models. We
have applied the particle MCMC of Andrieu et al. (2008) to univariate models and offer a
comparison with estimates from the SPF. [Edit]
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