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State-space models : applications in economics and finance

Author: Yong Zeng; Shu Wu
Publisher: New York : Springer, [2013] ©2013
Series: Statistics and econometrics for finance.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
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 andnon-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
State-space models
(OCoLC)841495241
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yong Zeng; Shu Wu
ISBN: 9781461477891 1461477891
OCLC Number: 858626314
Description: 1 online resource (xxi, 347 pages).
Contents: Particle Filtering and Parameter Learning in Nonlinear State-Space Models. Adaptive Filtering, Nonlinear State-Space Models, and Applications in Finance and Econometrics / Tze Leung Lai and Vibhav Bukkapatanam --
The Extended Liu and West Filter: Parameter Learning in Markov Switching Stochastic Volatility Models / Maria Paula Rios and Hedibert Freitas Lopes --
A Survey of Implicit Particle Filters for Data Assimilation / Alexandre J. Chorin, Matthias Morzfeld, and Xuemin Tu --
Linear State-Space Models in Macroeconomics and Finance. Model Uncertainty, State Uncertainty, and State-Space Models / Yulei Luo, Jun Nie, and Eric R. Young --
Hong Kong Inflation Dynamics: Trend and Cycle Relationships with the USA and China / Pym Manopimoke --
The State Space Representation and Estimation of a Time-Varying Parameter VAR with Stochastic Volatility / Taeyoung Doh and Michael Connolly --
A Statistical Investigation of Stock Return Decomposition Based on the State-Space Framework / Jun Ma and Mark E. Wohar --
Hidden Markov Models, Regime-Switching, and Mathematical Finance. A HMM Intensity-Based Credit Risk Model and Filtering / Robert J. Elliott and Tak Kuen Siu --
Yield Curve Modelling Using a Multivariate Higher-Order HMM / Xiaojing Xi and Rogemar Mamon --
Numerical Methods for Optimal Annuity Purchasing and Dividend Optimization Strategies under Regime-Switching Models: Review of Recent Results / Zhuo Jin and George Yin --
Trading a Mean-Reverting Asset with Regime Switching: An Asymptotic Approach / Eunju Sohn and Qing Zhang --
CPPI in the Jump-Diffusion Model / Mingming Wang and Allanus Tsoi --
Nonlinear State-Space Models for High Frequency Financial Data. An Asymmetric Information Modeling Framework for Ultra-High Frequency Transaction Data: A Nonlinear Filtering Approach / Yoonjung Lee --
Heterogenous Autoregressive Realized Volatility Model / Yazhen Wang and Xin Zhang --
Parameter Estimation via Particle MCMC for Ultra-High Frequency Models / Cai Zhu and Jian Hui Huang.
Series Title: Statistics and econometrics for finance.
Responsibility: Yong Zeng, Shu Wu, editors.

Abstract:

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 andnon-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 focuseson 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.

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