The Econometrics of Financial Duration Modeling

Abstract

We discuss estimation and inference in financial durations models. For the classical autoregressive conditional duration (ACD) models by Engle and Russell (1998, Econometrica 66, 1127-1162, we show the surprising result that the large sample behavior of likelihood estimators depends on the tail behavior of the durations. Even under stationarity, asymptotic normality breaks down for tail indices smaller than one. Instead, estimators are mixed Gaussian with non-standard rates of convergence. We exploit here the crucial fact that for duration data the number of observations within any time span is random. Our results apply to general econometric models where the number of observed events is random.