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Author  |
Leiva, V.; Saulo, H.; Leao, J.; Marchant, C. |
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Title |
A family of autoregressive conditional duration models applied to financial data |
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Year |
2014 |
Publication |
Computational Statistics & Data Analysis |
Abbreviated Journal |
Comput. Stat. Data Anal. |
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Volume |
79 |
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Pages |
175-191 |
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Keywords |
Birnbaum-Saunders distribution; EM algorithm; High-frequency data; Maximum likelihood estimator; Monte Carlo simulation |
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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. (C) 2014 Elsevier B.V. All rights reserved. |
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Address |
[Leiva, Victor; Marchant, Carolina] Univ Valparaiso, Inst Estadist, Valparaiso, Chile, Email: victor.leiva@yahoo.com |
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Publisher |
Elsevier Science Bv |
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English |
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ISSN |
0167-9473 |
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Notes |
WOS:000340139900013 |
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UAI @ eduardo.moreno @ |
Serial |
396 |
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