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Author (up) Leiva, V.; Saulo, H.; Leao, J.; Marchant, C. pdf  doi
  Title A family of autoregressive conditional duration models applied to financial data Type
  Year 2014 Publication Computational Statistics & Data Analysis Abbreviated Journal Comput. Stat. Data Anal.  
  Volume 79 Issue Pages 175-191  
  Keywords Birnbaum-Saunders distribution; EM algorithm; High-frequency data; Maximum likelihood estimator; Monte Carlo simulation  
  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.  
  Address [Leiva, Victor; Marchant, Carolina] Univ Valparaiso, Inst Estadist, Valparaiso, Chile, Email:  
  Corporate Author Thesis  
  Publisher Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0167-9473 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000340139900013 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 396  
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