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Non-parametric Concepts for Financial Time Series

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Statistics of Financial Markets

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Abstract

With the analysis of (financial) time series one of the most important goals is to produce forecasts. Using past observed data one would like to make some statements about the future mean, the future volatility, etc., i.e., one would like to estimate the expectation and variance of the underlying process conditional on the past. One method to produce such estimates will be introduced in this chapter.

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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). Non-parametric Concepts for Financial Time Series. In: Statistics of Financial Markets. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-10026-4_13

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  • DOI: https://doi.org/10.1007/978-3-662-10026-4_13

  • 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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