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Scaling analysis of multi-variate intermittent time series

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  • Kitt, Robert
  • Kalda, Jaan

Abstract

The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similar to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the case of asset prices, such scaling behaviour can be used for risk forecasts: the probability of observing next day a large price movement is (super-universally) inversely proportional to the length of the ongoing low-variability period. Finally, a method is devised for a multi-factor scaling analysis. We apply the simplest, two-factor model to equity index and trading volume time series.

Suggested Citation

  • Kitt, Robert & Kalda, Jaan, 2005. "Scaling analysis of multi-variate intermittent time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 480-492.
    • Handle: RePEc:eee:phsmap:v:353:y:2005:i:c:p:480-492
    DOI: 10.1016/j.physa.2005.01.038
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    References listed on IDEAS

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    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
    2. Fujiwara, Yoshi & Di Guilmi, Corrado & Aoyama, Hideaki & Gallegati, Mauro & Souma, Wataru, 2004. "Do Pareto–Zipf and Gibrat laws hold true? An analysis with European firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 197-216.
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    Cited by:

    1. Andria, Joseph & di Tollo, Giacomo & Kalda, Jaan, 2022. "The predictive power of power-laws: An empirical time-arrow based investigation," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).

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