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Detecting periods in which a time series model fails to predict the observed volatility

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Summary

The cumulative sums of squares (CUSUMSQ) provide a means for detecting change points in the volatility (variance) of time series. In this paper a new method for detecting such change points is proposed. The method is based on a combination of two existing algorithms and is intended to combine their positive features in a single algorithm. The results of a simulation experiment to compare the performance of the algorithms are presented and, as an example, the algorithm is applied to the CUSUMSQ of pseudo-residuals. This provides a method of detecting periods in which a time series model fails to predict the observed volatility.

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Acknowledgesments

The valuable suggestions of two anonymous reviewers are gratefully acknowledged. I would also like to thank Walter Zucchini for helpful comments and overall support.

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  1. Institut für Statistik und Ökonometrie, University of Göttingen, Platz der Göttinger Sieben 5, D-37073, Göttingen

    Andreas Stadie

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  1. Andreas Stadie
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Stadie, A. Detecting periods in which a time series model fails to predict the observed volatility. Computational Statistics 18, 375–386 (2003). https://doi.org/10.1007/BF03354604

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  • DOI: https://doi.org/10.1007/BF03354604

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