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Measuring relative volatility in high‐frequency data under the directional change approach

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  • Shengnan Li
  • Edward P. K. Tsang
  • John O'Hara

Abstract

We introduce a new approach in measuring relative volatility between two markets based on the directional change (DC) method. DC is a data‐driven approach for sampling financial market data such that the data are recorded when the price changes have reached a significant amplitude rather than recording data under a predetermined timescale. Under the DC framework, we propose a new concept of DC micro‐market relative volatility to evaluate relative volatility between two markets. Unlike the time‐series method, micro‐market relative volatility redefines the timescale based on the frequency of the observed DC data between the two markets. We show that it is useful for measuring the relative volatility in micro‐market activities (high‐frequency data).

Suggested Citation

  • 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.
    • Handle: RePEc:wly:isacfm:v:29:y:2022:i:2:p:86-102
    DOI: 10.1002/isaf.1510
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    References listed on IDEAS

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    Cited by:

    1. Ao, Han & Li, Munan, 2024. "Exploiting the potential of a directional changes-based trading algorithm in the stock market," Finance Research Letters, Elsevier, vol. 60(C).

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