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Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time

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  • Vladimir Petrov

    (Department of Banking and Finance, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland
    Current address: Department of Banking and Finance, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland.)

  • Anton Golub

    (Flov Technologies AG, Gotthardstrasse 26, 6300 Zug, Switzerland)

  • Richard Olsen

    (Lykke Corp., Alpenstrasse 9, 6300 Zug, Switzerland)

Abstract

We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates, as well as a stock market index. We demonstrate the long memory of instantaneous volatility computed in directional-change intrinsic time. The provided volatility estimation method can be adapted as a universal multiscale risk-management tool independent of the discreteness and the type of analysed high-frequency data.

Suggested Citation

  • Vladimir Petrov & Anton Golub & Richard Olsen, 2019. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time," JRFM, MDPI, vol. 12(2), pages 1-31, April.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:54-:d:219095
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    Cited by:

    1. Lars Stentoft, 2020. "Computational Finance," JRFM, MDPI, vol. 13(7), pages 1-4, July.
    2. Shengnan Li & Edward P. K. Tsang & John O'Hara, 2022. "Measuring relative volatility in high‐frequency data under the directional change approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 86-102, April.
    3. James B. Glattfelder & Anton Golub, 2022. "Bridging the Gap: Decoding the Intrinsic Nature of Time in Market Data," Papers 2204.02682, arXiv.org.

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