Statistics and Econometrics for Finance

State-Space Models

Applications in Economics and Finance

Editors: Zeng, Yong, Wu, Shu (Eds.)

  • First comprehensive​​ work to explore recent developments of state-space models in economics and finance
  • State-space modeling is taught in a wide range of subject areas from economics, finance, and statistics to probability theory and stochastic analysis
  • Features contributions from well-known academic and modeling experts
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About this book

State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data.  The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals.

About the authors

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.

Reviews

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)


Table of contents (15 chapters)

  • Adaptive Filtering, Nonlinear State-Space Models, and Applications in Finance and Econometrics

    Lai, Tze Leung (et al.)

    Pages 3-22

  • The Extended Liu and West Filter: Parameter Learning in Markov Switching Stochastic Volatility Models

    Rios, Maria Paula (et al.)

    Pages 23-61

  • A Survey of Implicit Particle Filters for Data Assimilation

    Chorin, Alexandre J. (et al.)

    Pages 63-88

  • Model Uncertainty, State Uncertainty, and State-Space Models

    Luo, Yulei (et al.)

    Pages 91-112

  • Hong Kong Inflation Dynamics: Trend and Cycle Relationships with the USA and China

    Manopimoke, Pym

    Pages 113-132

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-1-4614-7789-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4614-7788-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
State-Space Models
Book Subtitle
Applications in Economics and Finance
Editors
  • Yong Zeng
  • Shu Wu
Series Title
Statistics and Econometrics for Finance
Series Volume
1
Copyright
2013
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4614-7789-1
DOI
10.1007/978-1-4614-7789-1
Hardcover ISBN
978-1-4614-7788-4
Series ISSN
2199-093X
Edition Number
1
Number of Pages
XXI, 347
Topics