AUTOREGRESSIVE CONDITIONAL DURATION - A NEW MODEL FOR IRREGULARLY SPACED TRANSACTION DATA

This paper proposes a new statistical model for the analysis of data w hich arrive at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point proce sses with dependent arrival rates. The conditional intensity is develo ped and compared with other self-exciting processes. Because the model focuses on the expected duration between events, it is called the aut oregressive conditional duration (ACD) model. Asymptotic properties of the quasi maximum likelihood estimator are developed as a corollary t o ARCH model results. Strong evidence is provided for duration cluster ing for the financial transaction data analyzed; both deterministic ti me-of-day effects and stochastic effects are important. The model is a pplied to the arrival times of trades and therefore is a model of tran saction volume, and also to the arrival of other events such as price changes. Models for the volatility of prices are estimated with price- based durations, and examined from a market microstructure point of vi ew.