High Frequency Volatility Modelling: A Markov Switching Autoregressive Conditional Intensity Model
We develop a Markov Switching Autoregressive Conditional Intensity (MS ACI) model by extending the Autoregressive Conditional Intensity model (ACI) (Russell 1999) with a Markov switching structure. The model provides reliable parametric intraday volatility estimation through the absolute price change point process. We apply the Stochastic Approximation Expectation Maximization (Celeux and Diebolt 1992) algorithm to obtain maximum likelihood estimates of the model. By applying our model to the most recent intraday price duration data for IBM, JPM and KO, and incorporating a regime switching relationship between volatility, contemporaneous volume and bid ask spread, we discover two distinct regimes that have distinct diurnal pattern which can be explained by the diurnal information arrival pattern and the heterogeneous volume volatility relationship as documented in Bessembinder and Seguin (1993) Arago and Nieto (2005) Hussain (2011) We provide a high frequency measure of the probability of information shock to the market, and a measure of the impact of information on volatility.