A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model. (8th August 2016)

• Record Type: Journal Article
• Authors: Fernandes, Marcelo; Medeiros, Marcelo C.; Veiga, Alvaro
• Is Part Of: Econometric reviews. Volume 35:Number 7(2016)
• Page Start: 1221
• Page End: 1250
• Publication Date: 2016-08-08
• Subjects: Explosive regimes -- Neural networks -- Quasi-maximum likelihood -- Sieve estimation -- Smooth transition -- Stationarity; C22; Econometrics -- Periodicals; 330.015195
• DOI: 10.1080/07474938.2014.977071
• Languages: English
• ISSNs: 0747-4938

Abstract

Abstract : In this article, we propose a class of logarithmic autoregressive conditional duration (ACD)-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and asymmetries in financial durations. In particular, our functional coefficient logarithmic autoregressive conditional duration (FC-LACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing sufficient conditions for strict stationarity, we address model identifiability as well as the asymptotic properties of the quasi-maximum likelihood (QML) estimator for the FC-LACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate a semiparametric variant of the FC-LACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations.