State-Space Models

Yong Zeng is a professor in Department of Mathematics and Statistics at University of Missouri at Kansas City. His main research interest includes mathematical finance, financial econometrics, stochastic nonlinear filtering, and Bayesian statistical analysis. Notably, he developed the statistical analysis via filtering for financial ultra-high frequency data, where the model can be viewed as a random-arrival-time state space model. He has published in Mathematical Finance, International Journal of Theoretical and Applied Finance, Applied Mathematical Finance, IEEE Transactions on Automatic Control, Statistical Inference for Stochastic Processes, among others. He held visiting associate professor positions 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.Shu Wu is an associate professor in Department of Economics at University of Kansas. His main research areas are empirical macroeconomics and finance. He has held visiting positions at Federal Reserve Bank at Kansas City, City University of Hong Kong. His publications have appeared in Journal of Monetary Economics, Journal of Money, Credit and Banking, Macroeconomic Dynamics, International Journal of Theoretical and Applied Finance, Journal of International Financial Markets, Institutions and Money, Handbook of Quantitative Finance and Risk Management, Hidden Markov Models in Finance among others. He received his Ph.D. in economics from Stanford University in 2000.

From the book reviews:“The intention of this edited volume is to provide methodological development in state–space models, as well as study their applications, particularly in economics and finance. … this book has an impressive collection of material on useful and interesting topics regarding state–space models. The book will be useful equally to graduate students and researchers interested in space-modeling in statistical science, mathematics, and more importantly, in economics.” (Technometrics, Vol. 56 (2), May, 2014)

• Particle Filtering and Parameter Learning in Nonlinear State-Space Models.- Linear State-Space Models in Macroeconomics and Finance.- Hidden Markov Models, Regime-Switching, and Mathematical Finance.- Nonlinear State-Space Models for High Frequency Financial Data.- Index.