A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model |
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Authors: | Marcelo Fernandes Marcelo C Medeiros Alvaro Veiga |
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Institution: | 1. Sao Paulo School of Economics – FGV, Sao Paulo, Brazil, and School of Economics and Finance, Queen Mary University of London, London, UK;2. Department of Economics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil;3. Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil |
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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. |
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Keywords: | Explosive regimes Neural networks Quasi-maximum likelihood Sieve estimation Smooth transition Stationarity |
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