A (semi)parametric functional coefficient logarithmic autoregressive conditional duration model

Resumo

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.

Ficha do documento

Tipo

Artigo científico

Ano

2016

Instituição

Taylor & Francis Inc

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Acesso restrito

DOI

10.1080/07474938.2014.977071

Identificador

oai:repositorio.fgv.br:10438/23576

Temas

Economia

Palavras-chave

Explosive regimesNeural networksQuasi-maximum likelihoodSieve estimationSmooth transitionStationarityEconomiaDistribuição lognormalRedes neurais (Computação)