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State-space models : applications in economics and finance / [edited by] Yong Zeng and Shu Wu.

Contributor(s): Material type: TextTextSeries: Statistics and econometrics for financePublication details: New York : Springer, 2013.Description: xxi, 347 p. : illustrations (some color) ; 24 cmISBN:
  • 9781461477884 (hbk. : acidfree paper)
Subject(s): DDC classification:
  • 330.015195 23 Z54
Contents:
Part I. Particle Filtering and Parameter Learning in Nonlinear State-Space Models.- 1. Adaptive filtering, nonlinear state-space models, and applications in finance and econometrics-- 2. The extended liu and west filter: parameter learning in Markov switching stochastic volatility models-- 3. A survey of implicit particle filters for data assimilation-- Part II. Linear State-Space Models in Macroeconomics and Finance.- 4. Model uncertainty, state uncertainty, and state-space models-- 5. Hong Kong Inflation dynamics: trend and cycle relationships-- 6. The state space representation and estimation of a time-varying parameter VAR with stochastic volatility-- 7. A statistical investigation of stock return docomposition based on the stat-space framework-- Part III. Hidden Markov Models, Regime-Switching, and Mathematical Finance.- 8. A HMM intensity-based credit risk model and filtering-- 9. Yield curve modelling using a multivariate higher-order HMM-- 10. Numerical methods for optimal annuity purchasing and dividend optimization strategies under regime-switching models: review of recent results-- 11. Trading a mean-reverting asset with regime switching: an asymptotic approach-- 12. CPPI in the Jump-Diffusion model-- Part IV. Nonlinear State-Space Models for High Frequency Financial Data.- 13. An asymmetric information modeling framework for ultra-high frequency transaction data: a nonlinear filtering approach-- 14. Heterogeneous autoregressive realized volatility model-- 15. Parameter estimation via particle MCMC for ultra-high frequency models-- Index.
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 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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Includes bibliographical references and index.

Part I. Particle Filtering and Parameter Learning in Nonlinear State-Space Models.-
1. Adaptive filtering, nonlinear state-space models, and applications in finance and econometrics--
2. The extended liu and west filter: parameter learning in Markov switching stochastic volatility models--
3. A survey of implicit particle filters for data assimilation--

Part II. Linear State-Space Models in Macroeconomics and Finance.-
4. Model uncertainty, state uncertainty, and state-space models--
5. Hong Kong Inflation dynamics: trend and cycle relationships--
6. The state space representation and estimation of a time-varying parameter VAR with stochastic volatility--
7. A statistical investigation of stock return docomposition based on the stat-space framework--

Part III. Hidden Markov Models, Regime-Switching, and Mathematical Finance.-
8. A HMM intensity-based credit risk model and filtering--
9. Yield curve modelling using a multivariate higher-order HMM--
10. Numerical methods for optimal annuity purchasing and dividend optimization strategies under regime-switching models: review of recent results--
11. Trading a mean-reverting asset with regime switching: an asymptotic approach--
12. CPPI in the Jump-Diffusion model--

Part IV. Nonlinear State-Space Models for High Frequency Financial Data.-
13. An asymmetric information modeling framework for ultra-high frequency transaction data: a nonlinear filtering approach--
14. Heterogeneous autoregressive realized volatility model--
15. Parameter estimation via particle MCMC for ultra-high frequency models--
Index.

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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