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Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures

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  • Kyungsub Lee

Abstract

The Hawkes model is suitable for describing self and mutually exciting random events. In addition, the exponential decay in the Hawkes process allows us to calculate the moment properties in the model. However, due to the complexity of the model and formula, few studies have been conducted on the performance of Hawkes volatility. In this study, we derived a variance formula that is directly applicable under the general settings of both unmarked and marked Hawkes models for tick-level price dynamics. In the marked model, the linear impact function and possible dependency between the marks and underlying processes are considered. The Hawkes volatility is applied to the mid-price process filtered at 0.1-second intervals to show reliable results; furthermore, intraday estimation is expected to have high utilization in real-time risk management. We also note the increasing predictive power of intraday Hawkes volatility over time and examine the relationship between futures and stock volatilities.

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  • Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org.
    • Handle: RePEc:arx:papers:2207.05939
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    References listed on IDEAS

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    Cited by:

    1. Lee, Kyungsub, 2023. "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Finance Research Letters, Elsevier, vol. 55(PA).
    2. Kyungsub Lee, 2023. "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Papers 2304.11883, arXiv.org.
    3. Kyungsub Lee, 2024. "Discrete Hawkes process with flexible residual distribution and filtered historical simulation," Papers 2401.13890, arXiv.org.

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