A nonlinear autoregressive conditional duration model with applications to financial transaction data

This paper presents a new model that improves upon several inadequacies of the original autoregressive conditional duration (ACD) model considered in Engle and Russell (Econometrica 66(5) (1998) 1127–1162). We propose a threshold autoregressive conditional duration (TACD) model to allow the expected duration to depend nonlinearly on past information variables. Conditions for the TACD process to be ergodic and existence of moments are established. Strong evidence is provided to suggest that fast transacting periods and slow transacting periods of NYSE stocks have quite different dynamics. Based on the improved model, we identify multiple structural breaks in the transaction duration data considered, and those break points match nicely with real economic events.