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Clustering of volatility in variable diffusion processes

Author

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  • Gunaratne, Gemunu H.
  • Nicol, Matthew
  • Seemann, Lars
  • Török, Andrei

Abstract

Increments in financial markets have anomalous statistical properties including fat-tailed distributions and volatility clustering (i.e., the autocorrelation functions of return increments decay quickly but those of the squared increments decay slowly). One of the central questions in financial market analysis is whether the nature of the underlying stochastic process can be deduced from these statistical properties. We have shown previously that a class of variable diffusion processes has fat-tailed distributions. Here we show analytically that such models also exhibit volatility clustering. To our knowledge, this is the first case where clustering of volatility is proven analytically in a model.

Suggested Citation

  • Gunaratne, Gemunu H. & Nicol, Matthew & Seemann, Lars & Török, Andrei, 2009. "Clustering of volatility in variable diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4424-4430.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:20:p:4424-4430
    DOI: 10.1016/j.physa.2009.06.050
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    Citations

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    Cited by:

    1. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    2. Seemann, Lars & McCauley, Joseph L. & Gunaratne, Gemunu H., 2011. "Intraday volatility and scaling in high frequency foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 121-126, June.
    3. Hua, Jia-Chen & Chen, Lijian & Falcon, Liberty & McCauley, Joseph L. & Gunaratne, Gemunu H., 2015. "Variable diffusion in stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 221-233.

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