dc.contributorEscolas::EESP
dc.creatorFernandes, Marcelo
dc.creatorMedeiros, Marcelo C.
dc.creatorVeiga, Alvaro
dc.date.accessioned2013-12-09T12:13:21Z
dc.date.available2013-12-09T12:13:21Z
dc.date.created2013-12-09T12:13:21Z
dc.date.issued2013-12-09
dc.identifierTD 343
dc.identifierhttp://hdl.handle.net/10438/11334
dc.description.abstractIn this paper, we propose a class of ACD-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and sign and size asymmetries in financial durations. In particular, our functional coefficient autoregressive conditional duration (FC-ACD) 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 that the sufficient conditions for strict stationarity do not exclude explosive regimes, we address model identifiability as well as the existence, consistency, and asymptotic normality of the quasi-maximum likelihood (QML) estimator for the FC-ACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate using a sieve approach a semiparametric variant of the FC-ACD 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.
dc.languageeng
dc.relationEESP - Textos para Discussão;TD 343
dc.subjectExplosive regimes
dc.subjectNeural networks
dc.subjectQuasi-maximum likelihood
dc.subjectSieve
dc.titleA (semi-)parametric functional coefficient autoregressive conditional duration model
dc.typeWorking Paper


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