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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19955
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dc.contributor.authorKaranasos, M-
dc.contributor.authorYfanti, S-
dc.contributor.authorChristopoulos, A-
dc.date.accessioned2020-01-08T16:46:10Z-
dc.date.available2020-01-08T16:46:10Z-
dc.date.issued2020-01-04-
dc.identifierORCID iD: Menelaos Karanasos https://orcid.org/0000-0001-5442-3509-
dc.identifier.citationKaranasos, M., Yfanti, S. and Christopoulos, A. (2021) 'The long memory HEAVY process: modeling and forecasting financial volatility', Annals of Operations Research, 306 (1-2), pp. 111 - 130. doi: 10.1007/s10479-019-03493-8.en_US
dc.identifier.issn0254-5330-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/19955-
dc.description.abstractCopyright © The Author(s) 2020. This paper studies the bivariate HEAVY system of volatility regression equations and its various extensions that are directly applicable to the day-to-day business treasury operations of trading in foreign exchange and commodities, investing in bond and stock markets, hedging out market risk, and capital budgeting. We enrich the HEAVY framework with powers, asymmetries, and long memory that improve its forecasting accuracy significantly. Other findings are as follows. First, hyperbolic memory fits the realized measure better, whereas fractional integration is more suitable for the powered returns. Second, the structural breaks applied to the bivariate system capture the time-varying behavior of the parameters, in particular during and after the global financial crisis of 2007/2008.en_US
dc.format.extent111 - 130-
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rightsCopyright © The Author(s) 2020. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectasymmetriesen_US
dc.subjectstructural breaksen_US
dc.subjectrisk managementen_US
dc.subjectrealized varianceen_US
dc.subjectpower transformationsen_US
dc.subjectfinancial crisisen_US
dc.subjectforecastingen_US
dc.subjectlong memoryen_US
dc.subjecthigh-frequency dataen_US
dc.subjectHEAVY modelen_US
dc.titleThe long memory HEAVY process: modeling and forecasting financial volatilityen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1007/s10479-019-03493-8-
dc.relation.isPartOfAnnals of Operations Research-
pubs.issue1 - 2-
pubs.publication-statusPublished-
pubs.volume306-
dc.identifier.eissn1572-9338-
dc.rights.holderThe Author(s)-
Appears in Collections:Dept of Economics and Finance Research Papers

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