Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model
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114397683. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.
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| ID Code: | 234672 | ||
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| Item Type: | Contribution to Journal (Journal Article) | ||
| Refereed: | Yes | ||
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| Additional Information: | Acknowledgment: This research has been supported by Center for Research in Econometric Analysis of Time Series (CREATES), funded by the Danish National Research Foundation, Grant No. DNRF 78. Part of the work for this paper was carried out when the second author was visiting School of Economics and Finance of Queensland University of Technology, Brisbane, whose kind hospitality is gratefully acknowledged. The paper has been presented at the Advances in Statistics and Econometrics Workshop, University of Queensland, November 2016, the Humboldt-Aarhus-Xiamen Workshop, Aarhus University, January 2017, The European Meeting of Statisticians, Helsinki, July 2017, the Inaugural Conference of the HeiKaMEtrics Network, Heidelberg, September 2017, the 11th Annual SoFiE Conference, Lugano, June 2018, and in seminars at Monash University, University of Helsinki, University of Sydney and University of Tasmania, Hobart. Comments from participants of these events are gratefully acknowledged. Computational resources and services used in this work were provided by the High Performance Computing and Research Support Group, Queensland University of Technology. We wish to thank Glen Wade for valuable computational assistance. Any errors and shortcomings in this paper are our sole responsibility. | ||
| Measurements or Duration: | 16 pages | ||
| Keywords: | Deterministically varying correlation, Multiplicative time-varying GARCH, Multivariate GARCH, Nonstationary volatility, Smooth transition GARCH | ||
| DOI: | 10.1016/j.ecosta.2021.07.008 | ||
| ISSN: | 2452-3062 | ||
| Pure ID: | 114397683 | ||
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Business & Law Current > Schools > School of Economics & Finance |
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| Funding Information: | This research has been supported by Center for Research in Econometric Analysis of Time Series (CREATES), funded by the Danish National Research Foundation, Grant No. DNRF 78. Part of the work for this paper was carried out when the second author was visiting School of Economics and Finance of Queensland University of Technology, Brisbane, whose kind hospitality is gratefully acknowledged. The paper has been presented at the Advances in Statistics and Econometrics Workshop, University of Queensland, November 2016, the Humboldt-Aarhus-Xiamen Workshop, Aarhus University, January 2017, The European Meeting of Statisticians, Helsinki, July 2017, the Inaugural Conference of the HeiKaMEtrics Network, Heidelberg, September 2017, the 11th Annual SoFiE Conference, Lugano, June 2018, and in seminars at Monash University, University of Helsinki, University of Sydney and University of Tasmania, Hobart. Comments from participants of these events are gratefully acknowledged. Computational resources and services used in this work were provided by the High Performance Computing and Research Support Group, Queensland University of Technology. We wish to thank Glen Wade for valuable computational assistance. Any errors and shortcomings in this paper are our sole responsibility. This research has been supported by Center for Research in Econometric Analysis of Time Series (CREATES), funded by the Danish National Research Foundation, Grant No. DNRF 78. Part of the work for this paper was carried out when the second author was visiting School of Economics and Finance of Queensland University of Technology, Brisbane, whose kind hospitality is gratefully acknowledged. The paper has been presented at the Advances in Statistics and Econometrics Workshop, University of Queensland, November 2016, the Humboldt-Aarhus-Xiamen Workshop, Aarhus University, January 2017, The European Meeting of Statisticians, Helsinki, July 2017, the Inaugural Conference of the HeiKaMEtrics Network, Heidelberg, September 2017, the 11th Annual SoFiE Conference, Lugano, June 2018, and in seminars at Monash University, University of Helsinki, University of Sydney and University of Tasmania, Hobart. Comments from participants of these events are gratefully acknowledged. Computational resources and services used in this work were provided by the High Performance Computing and Research Support Group, Queensland University of Technology. We wish to thank Glen Wade for valuable computational assistance. Any errors and shortcomings in this paper are our sole responsibility. | ||
| Copyright Owner: | 2021 EcoSta Econometrics and Statistics | ||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
| Deposited On: | 16 Aug 2022 06:59 | ||
| Last Modified: | 29 Apr 2024 20:05 |
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