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

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  • Andreas Stadie

Abstract

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. Copyright Physica-Verlag 2003

Suggested Citation

  • Andreas Stadie, 2003. "Detecting periods in which a time series model fails to predict the observed volatility," Computational Statistics, Springer, vol. 18(3), pages 375-386, September.
    • Handle: RePEc:spr:compst:v:18:y:2003:i:3:p:375-386
    DOI: 10.1007/BF03354604
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    References listed on IDEAS

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    1. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    2. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    3. Bos, Theodore & Ding, David & Fetherston, Thomas A., 1998. "Searching for periods of volatility: A study of the behavior of volatility in Thai stocks," Pacific-Basin Finance Journal, Elsevier, vol. 6(3-4), pages 295-306, August.
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