Financial time series
Overview
Dimitris N. Politis
Published Online: Aug 19 2009 10:29 AM
DOI: 10.1002/wics.24
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Abstract
The evolution of financial markets is a complicated real-world phenomenon that ranks at the top in terms of difficulty of modeling and/or prediction. One reason for this difficulty is the well-documented nonlinearity that is inherent at work. The state-of-the-art on the nonlinear modeling of financial returns is given by the popular auto-regressive conditional heteroscedasticity (ARCH) models and their generalizations but they all have their short-comings. Foregoing the goal of finding the ‘best’ model, it is possible to simply transform the problem into a more manageable setting such as the setting of linearity. The form and properties of such a transformation are given, and the issue of one-step-ahead prediction using the new approach is explicitly addressed. Copyright © 2009 John Wiley & Sons, Inc.
Images
Figure 1. Daily returns of the S&P500 index spanning the period 8-30-1979 to 8-30-1991.
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Figure 2. (a) Correlogram of the S&P500 returns. (b) Correlogram of the S&P500 squared returns.
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Figure 3. Normalized S&P500 returns, i.e., the tranformed V -series, spanning the same period 8-30-1979 to 8-30-1991.
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Figure 4. Plot of \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\hat K(w_{1}, w_{2})$\end{document} vs. (w 1 , w 2 ) for the S&P500 returns.
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Figure 5. Plot of \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\hat K(w_{1}, w_{2})$\end{document} vs. (w 1 , w 2 ) for the normalized S&P500 returns.
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Figure 6. QQ-plot of the S&P500 returns.
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Figure 7. QQ-plot of the normalized S&P500 returns.
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