TY - JOUR
T1 - A family of autoregressive conditional duration models applied to financial data
AU - Leiva, Víctor
AU - Saulo, Helton
AU - Leão, Jeremias
AU - Marchant, Carolina
N1 - Funding Information:
The authors wish to thank the Editors and two anonymous referees for their constructive comments on an earlier version of this manuscript which resulted in this improved version. Also, the authors wish to thank Dr. C.R. Bhatti for providing the data used in the work and for the exchange of comments about the topic of the paper. This research was partially supported by FONDECYT 1120879 from the Chilean government, and CAPES , CNPq and FACEPE from the Brazilian government.
PY - 2014/11
Y1 - 2014/11
N2 - 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.
AB - 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.
KW - Birnbaum-Saunders distribution
KW - EM algorithm
KW - High-frequency data
KW - Maximum likelihood estimator
KW - Monte Carlo simulation
UR - http://www.scopus.com/inward/record.url?scp=84903216831&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2014.05.016
DO - 10.1016/j.csda.2014.05.016
M3 - Article
AN - SCOPUS:84903216831
SN - 0167-9473
VL - 79
SP - 175
EP - 191
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -