4. Nonlinear Models and Their Applications

  1. Ruey S. Tsay

Published Online: 2 AUG 2010

DOI: 10.1002/9780470644560.ch4

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) Nonlinear Models and Their Applications, in Analysis of Financial Time Series, Third Edition, Third Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470644560.ch4

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:

  • financial time series;
  • forecasting;
  • nonlinear models

Summary

This chapter focuses on nonlinearity in financial data and nonlinear econometric models useful in analysis of financial time series. Many nonlinear time series models have been proposed in the statistical literature, such as the bilinear models, the threshold autoregressive (TAR) model, the state-dependent model, and the Markov switching model. Recently, a number of nonlinear models have been proposed by making use of advances in computing facilities and computational methods. Examples of such extensions include the nonlinear state-space modeling, the functional coefficient autoregressive model, the nonlinear additive autoregressive model, and the multivariate adaptive regression spline. The chapter discusses some nonlinear models that are applicable to financial time series. The discussion includes some nonparametric and semiparametric methods. The chapter reviews some nonlinearity tests and discusses modeling and forecasting of nonlinear models. Finally, it gives an application of nonlinear models.

Controlled Vocabulary Terms

autoregressive models; forecasting; multivariate adaptive regression splines; non-parametric methods; nonlinear time series analysis