A family of autoregressive conditional duration models applied to financial data

Víctor Leiva, Helton Saulo, Jeremias Leão, Carolina Marchant

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)175-191
Number of pages17
JournalComputational Statistics and Data Analysis
Volume79
DOIs
StatePublished - Nov 2014
Externally publishedYes

Keywords

  • Birnbaum-Saunders distribution
  • EM algorithm
  • High-frequency data
  • Maximum likelihood estimator
  • Monte Carlo simulation

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