Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data

Abstract

This paper proposes a new statistical model for the analysis of data which arrive at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point processes with dependent arrival rates. The conditional intensity is developed and compared with other self-exciting processes. Because the model focuses on the expected duration between events, it is called the autoregressive conditional duration (ACD) model. Asymptotic properties of the quasi maximum likelihood estimator are developed as a corollary to ARCH model results. Strong evidence is provided for duration clustering for the financial transaction data analyzed; both deterministic time-of-day effects and stochastic effects are important. The model is applied to the arrival times of trades and therefore is a model of transaction 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 view.

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

“…Examples of autoregressive conditional parameter models include the generalised autoregressive conditional heteroscedasticity (GARCH) model of Bollerslev (1986), the autoregressive conditional duration (ACD) and intensity (ACI) models of Engle and Russell (1998), the autoregressive conditional Poisson model of Rydberg and Shephard (2000), the dynamic conditional correlation model of Engle (2002), specific autoregressive copulas in Patton (2006), and the HEAVY model of Shephard and Sheppard (2010). Due to their widespread use, the class of ACP models provides a useful benchmark to our analysis.…”

Dynamic Latent Factor Models for Intensity Processes

“…Since the introduction of the autoregressive conditional duration (ACD) model proposed by Engle and Russell (1998) and Engle (2000), much research has been devoted to the specification and application of discrete time autoregressive duration models (see Bauwens and Giot, 2000, Grammig and Maurer, 2000, Dufour and Engle, 2000, Fernandes and Grammig, 2001, and Hautsch, 2002. An obvious reason for the focus on duration models is that the inclusion of dynamic structures, which is essential for modelling financial point processes, is quite straightforward.…”

Decision-making in incomplete markets with ambiguity—a case study of a gas field acquisition

“…They also compare different dynamic copula models and conclude that the likelihood information is extensively exploited under a GAS framework.As shown inAndres (2014), the model with dynamic scores outperforms autoregressive conditional duration (ACD) models in terms of the rate of convergence and reliability. Note that an ACD model, as proposed in Engle and Russell (1998), is analogous to a Gaussian GARCH model. Furthermore, financial data often contain fat-tails: extreme outcomes happen too often for a normal distribution to be capable of accounting for the outliers.…”