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ARIMA Time Series Models

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Statistics of Financial Markets

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Abstract

In this chapter we will deal with the classical, linear time series analysis. At first we will define the general linear process.

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Recommended Literature

  • Schlittgen, R. and Streitberg, B. (1995). Zeitreihenanalyse, Oldenbourg.

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  • Gouriéroux, C. (1997). ARCH Models and Financial Applications,Springer Verlag.

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  • Gouriéroux, C. and Jasiak, J. (2002). Nonlinear autocorrelograms• An application to inter-trade durations, Journal of Time Series Analysis 23: 127154.

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  • Gouriéroux, C. and Monfort, A. (1992). Qualitative threshold ARCH models, Journal of Econometrics 52: 159–199.

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  • Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.

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  • Hamilton, J. D. (1994). Time Series Analysis,Princeton Univ Press, Princeton, New Jersey.

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Author information

Authors and Affiliations

  1. University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Professor Dr. Jürgen Franke

  2. CASE-Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Str. 1, 10178, Berlin, Germany

    Professor Dr. Wolfgang Härdle

  3. Econometric Institute, Faculty of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands

    Professor Dr. Christian M. Hafner

Authors
  1. Professor Dr. Jürgen Franke
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  2. Professor Dr. Wolfgang Härdle
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  3. Professor Dr. Christian M. Hafner
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© 2004 Springer-Verlag Berlin Heidelberg

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Franke, J., Härdle, W., Hafner, C.M. (2004). ARIMA Time Series Models. In: Statistics of Financial Markets. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-10026-4_11

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  • DOI: https://doi.org/10.1007/978-3-662-10026-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21675-9

  • Online ISBN: 978-3-662-10026-4

  • eBook Packages: Springer Book Archive

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