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Modeling Dependency Of Volatility On Sampling Frequency Via Delay Equations

Author

Listed:
  • CHUONG LUONG

    (Department of Mathematics & Statistics, Curtin University, GPO Box U1987, Perth, 6845 Western Australia)

  • NIKOLAI DOKUCHAEV

    (Department of Mathematics & Statistics, Curtin University, GPO Box U1987, Perth, 6845 Western Australia)

Abstract

The paper studies the modeling of time series with the prescribed dependence of the volatility on the sampling frequency. This dependence is often observed for financial time series. We suggest to model the dependence of volatility on sampling frequency via delay equations for the underlying prices. It appears that these equations allow to model the price processes with volatility that increases when the sampling rates increase. In addition, these equations are able to model the inverse phenomena where the volatility decreases with the increase in sampling frequencies.

Suggested Citation

  • Chuong Luong & Nikolai Dokuchaev, 2016. "Modeling Dependency Of Volatility On Sampling Frequency Via Delay Equations," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-21, June.
    • Handle: RePEc:wsi:afexxx:v:11:y:2016:i:02:n:s201049521650007x
    DOI: 10.1142/S201049521650007X
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    References listed on IDEAS

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    Cited by:

    1. Chuong Luong & Nikolai Dokuchaev, 2018. "Forecasting of Realised Volatility with the Random Forests Algorithm," JRFM, MDPI, vol. 11(4), pages 1-15, October.
    2. Nikolai Dokuchaev, 2017. "A pathwise inference method for the parameters of diffusion terms," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 731-743, October.

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