A semiparametric conditional duration model

Abstract

We propose a new semiparametric autoregressive duration (SACD) model, which incorporates the parametric and nonparametric estimators of the conditional duration in a multiplicative way. Asymptotic properties for this combined estimator are presented. Empirical applications to the transaction duration of the U.S. 2-Year Treasury note show the outperformance of our SACD models over parametric ACD models.

Conditional Duration Models for High‐frequency Data: A Review on Recent Developments

“…Dungey et al . () propose another semiparametric autoregressive duration (SACD) model, inspired by the univariate and multivariate volatility works of Mishra et al . () and Long et al .…”

“…An empirical evidence in support of the robust finite sample performance of the SEMI-ACD model is provided by applying it to study the price duration process in the foreign exchange market 21 published over the Reuters' network, specifically, the US dollar to Euro exchange rate data. Dungey et al (2014) propose another semiparametric autoregressive duration (SACD) model, inspired by the univariate and multivariate volatility works of Mishra et al (2010) and Long et al (2011), respectively. The SACD incorporates the parametric and nonparametric estimators of the conditional duration in a multiplicative way and its estimators are consistent.…”

“…This is, possibly, the first study that aims at obtaining a combined estimator of the conditional duration. Dungey et al (2014) first model the ACD parametrically, and then model the conditional mean of the standardized parametric residuals nonparametrically. The estimate of the latter serves as a nonparametric correction factor for the parametric ACD estimator.…”