Autoregressive Conditional Intensity Model Research Articles

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.

Who moves first? An intensity-based measure for information flows across stock exchanges

In this paper we propose an innovative measure for information flows between stock exchanges. We develop an intensity-based information share using Russell’s (1999) autoregressive conditional intensity model. Thereby we maintain the irregular nature of financial high frequency data and use durations and timing of price changes to determine the informationally dominant market. From our empirical application to US-listed Canadian stocks we conclude that the home market mostly reflects information first. On the basis of a cross-sectional analysis we find a positive correlation between the intensity-based information share and liquidity.

International Price Discovery in Stock Markets - A Unique Intensity Based Information Share

This paper proposes a new information share for price discovery based on Russell's (1999) autoregressive conditional intensity model. While previous studies rely on equally spaced high frequency data, we use the information conveyed by trade intensities to determine a market's contribution to price discovery. Thereby, we account for the irregular nature of transaction data. Moreover, in contrast to the commonly applied Hasbrouck (1995) approach, which yields lower and upper bounds for information shares, our model delivers a unique measure. Our empirical application to US-listed Canadian stocks supports previous evidence for the home market leadership in price discovery.

MODELING HIGH-FREQUENCY FOREIGN EXCHANGE DATA DYNAMICS

This paper shows that high-frequency, irregularly spaced, foreign exchange (FX) data can generate nonnormality, conditional heteroskedasticity, and leptokurtosis when aggregated into fixed-interval calendar time, even when these features are absent in the original DGP. Furthermore, we introduce a new approach to modeling these high-frequency irregularly spaced data based on the Poisson regression model. The new model is called the autoregressive conditional intensity model and it has the advantage of being simple and of maintaining the calendar timescale. To illustrate the virtues of this approach, we examine a classical issue in FX microstructure: the variation in information content as a function of fluctuations in the intensity of activity levels.