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Non-Parametric and Flexible Time Series Estimators

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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 data one can argue about the future mean, the future volatility and so on; however, a flexible method of producing such estimates will be introduced in this chapter.

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Authors and Affiliations

  1. Department of Mathematics, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Jürgen Franke

  2. Ladislaus von Bortkiewicz Chair of Statistics, Humboldt-Universität Berlin, Berlin, Germany

    Wolfgang Karl Härdle

  3. Louvain Institute of Data Analysis and Modeling in Economics and Statistics, UCLouvain, Louvain-la-Neuve, Belgium

    Christian Matthias Hafner

Authors
  1. Jürgen Franke
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  2. Wolfgang Karl Härdle
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  3. Christian Matthias Hafner
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Franke, J., Härdle, W.K., Hafner, C.M. (2019). Non-Parametric and Flexible Time Series Estimators. In: Statistics of Financial Markets. Universitext. Springer, Cham. https://doi.org/10.1007/978-3-030-13751-9_15

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