Autoregressive conditional duration modelling of high frequency data

2021/11/03 by Xiufeng Yan, Yan, Xiufeng
Economics, Econometrics and Finance · #Applications (stat.AP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Monetary Policy and Economic Impact #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2111.02300

openalex publication_date 2021/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper explores the duration dynamics modelling under the Autoregressive Conditional Durations (ACD) framework (Engle and Russell 1998). I test different distributions assumptions for the durations. The empirical results suggest unconditional durations approach the Gamma distributions. Moreover, compared with exponential distributions and Weibull distributions, the ACD model with Gamma distributed innovations provide the best fit of SPY durations.

Citations

• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Generalized autoregressive conditional heteroskedasticity
• The Behavior of Stock-Market Prices
• A Tale of Two Time Scales

Related

• Asymptotics for the Generalized Autoregressive Conditional Duration Model
• Beyond the Mean: Limit Theory and Tests for Infinite-Mean Autoregressive Conditional Durations
• On a quantile autoregressive conditional duration model applied to high-frequency financial data
• The Econometrics of Financial Duration Modeling
• Effective Measure of Endogeneity for the Autoregressive Conditional Duration Point Processes via Mapping to the Self-Excited Hawkes Process