Semiparametric Duration Models

Abstract

In this article we consider semiparametric duration models and efficient estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too restrictive to describe financial durations accurately. Therefore, we consider semiparametric extensions of the standard specification that allow for arbitrary kinds of dependencies between the innovations. The exact nonparametric specification of these dependencies determines the flexibility of the semiparametric model. We calculate semiparametric efficiency bounds for the ACD parameters, discuss the construction of efficient estimators, and study the efficiency loss of the exponential pseudolikelihood procedure. This efficiency loss proves to be sizeable in applications. For durations observed on the Paris Bourse for the Alcatel stock in July and August 1996, the proposed semiparametric procedures clearly outperform pseudolikelihood procedures. We analyze these efficiency gains using a simulation study confirming that, at least at the Paris Bourse, dependencies among rescaled durations can be exploited.

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