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

Li, Y. and Nolte, I. and Nolte, S. (2021) High-frequency volatility modeling : A Markov-Switching Autoregressive Conditional Intensity model. Journal of Economic Dynamics and Control, 124: 104077. ISSN 0165-1889

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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.

Item Type:

Journal Article

Journal or Publication Title:

Journal of Economic Dynamics and Control

Additional Information:

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

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2600/2606

Subjects:

?? intensity modelinginvarianceregime switchstock return volatilitycontrol and optimizationeconomics and econometricsapplied mathematics ??

Departments:

Lancaster University Management School > Accounting & Finance

ID Code:

152154

Deposited By:

ep_importer_pure

Deposited On:

01 Mar 2021 09:35

Refereed?:

Yes

Published?:

Published

Last Modified:

26 Nov 2024 01:58

URI:

https://eprints.lancs.ac.uk/id/eprint/152154