11. State-Space Models and Kalman Filter

  1. Ruey S. Tsay

Published Online: 2 AUG 2010

DOI: 10.1002/9780470644560.ch11

Analysis of Financial Time Series, Third Edition, Third Edition

Analysis of Financial Time Series, Third Edition, Third Edition

How to Cite

Tsay, R. S. (2010) State-Space Models and Kalman Filter, in Analysis of Financial Time Series, Third Edition, Third Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470644560.ch11

Author Information

  1. The University of Chicago Booth School of Business, Chicago, IL, USA

Publication History

  1. Published Online: 2 AUG 2010
  2. Published Print: 13 AUG 2010

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470414354

Online ISBN: 9780470644560

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Keywords:

  • ARMA Models;
  • Kalman filter;
  • smoothing methods;
  • state-space models;
  • time series analysis

Summary

The state-space model provides a flexible approach to time series analysis, especially for simplifying maximum-likelihood estimation and handling missing values. This chapter discusses the relationship between the state-space model and the ARIMA model, the Kalman filter algorithm, various smoothing methods, and some applications. It begins with a simple model that shows the basic ideas of the state-space approach to time series analysis before introducing the general state-space model. For demonstrations, the chapter uses the model to analyze realized volatility series of asset returns, the time-varying coefficient market models, and the quarterly earnings per share of a company. Finally, it considers some applications of the state-space model in finance and business to highlight the applicability of the model and to demonstrate the practical implementation of the analysis in S-Plus with SsfPack.

Controlled Vocabulary Terms

ARIMA model; Kalman filter; Smoothing; time series analysis