A family of autoregressive conditional duration models applied to financial data

Víctor Leiva, Helton Saulo, Jeremias Leão, Carolina Marchant

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

41 Citas (Scopus)

Resumen

The Birnbaum-Saunders distribution is receiving considerable attention due to its good properties. One of its extensions is the class of scale-mixture Birnbaum-Saunders (SBS) distributions, which shares its good properties, but it also has further properties. The autoregressive conditional duration models are the primary family used for analyzing high-frequency financial data. We propose a methodology based on SBS autoregressive conditional duration models, which includes in-sample inference, goodness-of-fit and out-of-sample forecast techniques. We carry out a Monte Carlo study to evaluate its performance and assess its practical usefulness with real-world data of financial transactions from the New York stock exchange.

Idioma originalInglés
Páginas (desde-hasta)175-191
Número de páginas17
PublicaciónComputational Statistics and Data Analysis
Volumen79
DOI
EstadoPublicada - nov 2014
Publicado de forma externa

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