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Article: A New Volatility Model: GQARCH-It\^{o} Model.

TitleA New Volatility Model: GQARCH-It\^{o} Model.
Authors
Issue Date2021
Citation
Journal of Time Series Analysis, 2021, v. 43, p. 345-370 How to Cite?
AbstractVolatility asymmetry is a hot topic in high-frequency financial market. This article proposes a new econometric model, which could describe volatility asymmetry based on high-frequency data and low-frequency data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties.We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed model and method. And a real data example is demonstrated that the new model has more substantial volatility prediction power than GARCH-Itô model in the literature.
Persistent Identifierhttp://hdl.handle.net/10722/314821

 

DC FieldValueLanguage
dc.contributor.authorYuan, H-
dc.date.accessioned2022-08-05T09:35:12Z-
dc.date.available2022-08-05T09:35:12Z-
dc.date.issued2021-
dc.identifier.citationJournal of Time Series Analysis, 2021, v. 43, p. 345-370-
dc.identifier.urihttp://hdl.handle.net/10722/314821-
dc.description.abstractVolatility asymmetry is a hot topic in high-frequency financial market. This article proposes a new econometric model, which could describe volatility asymmetry based on high-frequency data and low-frequency data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties.We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed model and method. And a real data example is demonstrated that the new model has more substantial volatility prediction power than GARCH-Itô model in the literature.-
dc.languageeng-
dc.relation.ispartofJournal of Time Series Analysis-
dc.titleA New Volatility Model: GQARCH-It\^{o} Model.-
dc.typeArticle-
dc.identifier.emailYuan, H: huilyuan@hku.hk-
dc.identifier.hkuros335036-
dc.identifier.volume43-
dc.identifier.spage345-
dc.identifier.epage370-

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