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  • SSRN-id2785499

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 124, 2021 DOI: 10.1016/j.jedc.2021.104077

    Accepted author manuscript, 6.07 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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High-frequency volatility modelling: a Markov-switching autoregressive conditional intensity model

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
Article number104077
<mark>Journal publication date</mark>31/03/2021
<mark>Journal</mark>Journal of Economic Dynamics and Control
Volume124
Number of pages21
Publication StatusPublished
Early online date28/01/21
<mark>Original language</mark>English

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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 124, 2021 DOI: 10.1016/j.jedc.2021.104077