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Conference Paper: A New Long Memory Volatility Model

TitleA New Long Memory Volatility Model
Authors
Issue Date2010
PublisherAmerican Statistical Association.
Citation
The 2010 Joint Statistical Meetings (JSM 2010), Vancouver, BC., Canada, 31 July-5 August 2010 How to Cite?
AbstractThis paper proposes a new type of long memory volatility model by mixing a common GARCH and a hyperbolic decaying structures. It is superior the commonly used FIGARCH and HYGARCH models since the variance of the hyperbolic structure is finite and that of the whole process may be infinite. Some probabilistic properties and the quasi-maximum likelihood estimation are also developed. The simulation experiments and a real example give further support to this new model.
DescriptionFinancial Econometrics and Risk Management — Topic Contributed Papers ; IMS, Section on Risk Analysis : Abstract - #308097
Persistent Identifierhttp://hdl.handle.net/10722/241358

 

DC FieldValueLanguage
dc.contributor.authorLi, G-
dc.date.accessioned2017-06-08T03:41:33Z-
dc.date.available2017-06-08T03:41:33Z-
dc.date.issued2010-
dc.identifier.citationThe 2010 Joint Statistical Meetings (JSM 2010), Vancouver, BC., Canada, 31 July-5 August 2010-
dc.identifier.urihttp://hdl.handle.net/10722/241358-
dc.descriptionFinancial Econometrics and Risk Management — Topic Contributed Papers ; IMS, Section on Risk Analysis : Abstract - #308097-
dc.description.abstractThis paper proposes a new type of long memory volatility model by mixing a common GARCH and a hyperbolic decaying structures. It is superior the commonly used FIGARCH and HYGARCH models since the variance of the hyperbolic structure is finite and that of the whole process may be infinite. Some probabilistic properties and the quasi-maximum likelihood estimation are also developed. The simulation experiments and a real example give further support to this new model. -
dc.languageeng-
dc.publisherAmerican Statistical Association. -
dc.relation.ispartofJoint Statistical Meetings, Vancouver, JSM 2010-
dc.titleA New Long Memory Volatility Model-
dc.typeConference_Paper-
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, G=rp00738-
dc.identifier.hkuros187379-
dc.publisher.placeUnited States-

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