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Author (up) Leiva, V.; Saulo, H.; Leao, J.; Marchant, C.
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: victor.leiva@yahoo.com
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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Author (up) Palma, W.; Bondon, P.; Tapia, J.
Title Assessing influence in Gaussian long-memory models Type
Year 2008 Publication Computational Statistics & Data Analysis Abbreviated Journal Comput. Stat. Data Anal.
Volume 52 Issue 9 Pages 4487-4501
Keywords
Abstract A statistical methodology for detecting influential observations in long-memory models is proposed. The identification of these influential points is carried out by case-deletion techniques. In particular, a Kullback-Leibler divergence is considered to measure the effect of a subset of observations on predictors and smoothers. These techniques are illustrated with an analysis of the River Nile data where the proposed methods are compared to other well-known approaches such as the Cook and the Mahalanobis distances. (c) 2008 Elsevier B.V. All rights reserved.
Address [Bondon, Pascal] Univ Paris 11, CNRS, UMR 8506, F-91192 Gif Sur Yvette, France, Email: bondon@lss.supelec.fr
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:000257014000023 Approved
Call Number UAI @ eduardo.moreno @ Serial 40
Permanent link to this record