High-Frequency Volatility Modelling: A Markov-Switching Autoregressive Conditional Intensity Model

Yifan Li, Ingmar Nolte, Sandra Nolte

Research output: Contribution to journalArticlepeer-review

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-specic news arrivals. We propose a novel daily volatility decomposition based on the two detected volatility regimes.
Original languageEnglish
Article number104077
JournalJournal of Economic Dynamics and Control
Volume124
DOIs
Publication statusPublished - 28 Jan 2021

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