High-frequency volatility modeling: A Markov-Switching Autoregressive Conditional Intensity model
نویسندگان
چکیده
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 to high-frequency transaction data, we detect two distinct regimes in intraday volatility process: dominant regime is observable throughout trading day representing risk-transferring activity of investors, minor concentrates around market liquidity shocks which mainly capture impacts firm-specific news arrivals. propose novel daily decomposition based on detected regimes.
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ژورنال
عنوان ژورنال: Journal of Economic Dynamics and Control
سال: 2021
ISSN: ['1879-1743', '0165-1889']
DOI: https://doi.org/10.1016/j.jedc.2021.104077