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High-frequency volatility modeling: A Markov-Switching Autoregressive Conditional Intensity model

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  • Li, Yifan
  • Nolte, Ingmar
  • Nolte, Sandra

Abstract

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional probability, and show that it can be reliably estimated via the Stochastic Approximation Expectation–Maximization algorithm. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm-specific news arrivals. We propose a novel daily volatility decomposition based on the two detected volatility regimes.

Suggested Citation

  • Li, Yifan & Nolte, Ingmar & Nolte, Sandra, 2021. "High-frequency volatility modeling: A Markov-Switching Autoregressive Conditional Intensity model," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
    • Handle: RePEc:eee:dyncon:v:124:y:2021:i:c:s0165188921000129
    DOI: 10.1016/j.jedc.2021.104077
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    Citations

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    Cited by:

    1. Greeshma Balabhadra & El Mehdi Ainasse & Pawel Polak, 2023. "High-Frequency Volatility Estimation with Fast Multiple Change Points Detection," Papers 2303.10550, arXiv.org, revised Jun 2024.
    2. Endres, Sylvia & Stübinger, Johannes, 2018. "A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns," FAU Discussion Papers in Economics 07/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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