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.

我们开发了具有时变过渡概率的马尔可夫切换自回归条件强度（MS-ACI）模型，并表明可以通过随机近似期望最大化算法可靠地对其进行估计。将我们的模型应用于高频交易数据，我们在日内波动过程中检测到两种不同的机制：在整个交易日中都可以观察到的代表投资者风险转移交易活动的主导波动机制，以及集中于左右波动的次要波动机制。市场流动性冲击，主要反映公司特定新闻到达的影响。我们提出了一种基于两个检测到的波动率制度的新颖的每日波动率分解。