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Financial time series

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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. This article is categorized under: Applications of Computational Statistics > Computational Finance

Daily returns of the S&P500 index spanning the period 8‐30‐1979 to 8‐30‐1991.

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QQ‐plot of the normalized S&P500 returns.

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QQ‐plot of the S&P500 returns.

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Plot of vs. (w1, w2) for the normalized S&P500 returns.

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Plot of vs. (w1, w2) for the S&P500 returns.

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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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(a) Correlogram of the S&P500 returns. (b) Correlogram of the S&P500 squared returns.

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Applications of Computational Statistics > Computational Finance

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