【Academic Seminar】Bayesian Inference via Filtering Equations for Financial Ultrahigh Frequency Data - Prof. Yong Zeng
Topic: Bayesian Inference via Filtering Equations for Financial Ultrahigh Frequency Data
Speaker: Prof. Yong Zeng, The University of Missouri at Kansas City
Date and Time: 14:00 pm - 15:00 pm, Thursday, July 13, 2019
Venue: Room 110, Zhi Xin Building
We propose a general partially-observed framework of Markov processes with marked point process observations for ultrahigh frequency (UHF) transaction price data, allowing other observable economic or market factors. We develop the corresponding Bayesian inference via filtering equations to quantify parameter and model uncertainty. Specifically, we derive the filtering equations to characterize the evolution of the statistical foundation such as likelihoods, posteriors, Bayes factors and posterior model probabilities. Given the computational challenge, we provide a weak convergence theorem, enabling us to employ the Markov chain approximation method to construct consistent, easily-parallelizable, recursive algorithms. The algorithms calculate the fundamental statistical characteristics and implement the Bayesian inference for streaming UHF data. The general theory is illustrated by specific models built for U.S. Treasury Notes transactions data from GovPX. This talk consists joint works with G. X. Hu, and D. Kuipers.
Yong Zeng is a professor in Department of Mathematics and Statistics at University of Missouri at Kansas City. His research interest includes statistical inference for stochastic processes, stochastic filtering and control, mathematical finance, financial econometrics, and statistics with related applications to genetics and network traffic modeling. He has held visiting professorships at Princeton University and the University of Tennessee. He received his B.S. from Fudan University in 1990, M.S. from University of Georgia in 1994 and Ph.D. from University of Wisconsin at Madison in 1999. All degrees were in statistics.