Statistical Analysis of the Filtering Model for Financial
Ultra-High Frequency Data
Yong Zeng (University of Missouri, Kansas City) Abstract: In this talk, we propose a general filtering model with marked Poisson
process observations for financial UHF data. The model can allow stochastic
volatility as well as inputs from other observable factors such as signed
order flow, and encompasses important existing models. The statistical
foundations of the proposed model - likelihoods, posterior, likelihood
ratios and Bayes factors - are studied. They are characterized by stochastic
partial differential equations such as filtering equations. Bayesian inference
(estimation and model selection) via filtering is studied. Convergence
theorems for consistent, efficient algorithms are established. Two general
approaches for constructing algorithms are discussed. One approach is
Markov chain approximation method, and the other is sequential Monte Carlo
or particle filtering. If time permits, examples of simulation, stock
price and treasury data would be provided. |