# Autoregressive Conditional Duration (ACD) Model

An Autoregressive Conditional Duration (ACD) Model is a statistical model for the data analysis of irregularly spaced and autorcorrelated intertrade time durations.

**Counter-Example(s):****See:**Financial Markets, Econometrics, Auction.

## References

### 2017

- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Autoregressive_conditional_duration Retrieved:2017-7-1.
- In financial econometrics, an
**autoregressive conditional duration**(**ACD**, Engle and Russell (1998)) model considers irregularly spaced and autocorrelated intertrade durations. ACD is analogous to GARCH. Indeed, in a continuous double auction (a common trading mechanism in many financial markets) waiting times between two consecutive trades vary at random.

- In financial econometrics, an

### 2009

- (Tsay, 2009) ⇒ Tsay, R. S. (2009). Autoregressive conditional duration models. In Palgrave handbook of econometrics (pp. 1004-1024). Palgrave Macmillan UK.DOI:10.1057/9780230244405_21 Free PDF
- ABSTRACT: This chapter studies the autoregressive conditional duration model. It discusses properties and statistical inference of the model. It also considers some extensions to handle nonlinear durations and interventions. For applications, we apply the model to daily range of the log price of Apple stock and find that adopting the decimal system for the US stock price on January 29, 2001, significantly reduces price volatility.

### 1998

- (Engle & Russell, 1998) ⇒ Engle, R. F., & Russell, J. R. (1998). Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica, 1127-1162. DOI: 10.2307/2999632
- 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.