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    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

    Embargo ends: 28/01/23

    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

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High-frequency volatility modelling : a Markov-switching autoregressive conditional intensity model. / Li, Yifan; Nolte, Ingmar; Nolte, Sandra.

In: Journal of Economic Dynamics and Control, Vol. 124, 104077, 31.03.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Li Y, Nolte I, Nolte S. High-frequency volatility modelling: a Markov-switching autoregressive conditional intensity model. Journal of Economic Dynamics and Control. 2021 Mar 31;124:104077. Epub 2021 Jan 28. doi: 10.1016/j.jedc.2021.104077

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Bibtex

@article{2c0f70ab6ee440a7b23f12ac337162d1,
title = "High-frequency volatility modelling: a Markov-switching autoregressive conditional intensity model",
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.",
author = "Yifan Li and Ingmar Nolte and Sandra Nolte",
note = "This is the author{\textquoteright}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",
year = "2021",
month = mar,
day = "31",
doi = "10.1016/j.jedc.2021.104077",
language = "English",
volume = "124",
journal = "Journal of Economic Dynamics and Control",
issn = "0165-1889",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - High-frequency volatility modelling

T2 - a Markov-switching autoregressive conditional intensity model

AU - Li, Yifan

AU - Nolte, Ingmar

AU - Nolte, Sandra

N1 - 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

PY - 2021/3/31

Y1 - 2021/3/31

N2 - 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.

AB - 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.

U2 - 10.1016/j.jedc.2021.104077

DO - 10.1016/j.jedc.2021.104077

M3 - Journal article

VL - 124

JO - Journal of Economic Dynamics and Control

JF - Journal of Economic Dynamics and Control

SN - 0165-1889

M1 - 104077

ER -