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
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -