Multivariate Hyper-Rotated GARCH-BEKK

Manabu Asai*, Michael McAleer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the 'curse of dimensionality'. Recently, Laurent, Rombouts and Violante (2014 "Multivariate Rotated ARCH Models"Journal of Econometrics 179: 16-30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters.

Original languageEnglish
Pages (from-to)175-198
Number of pages24
JournalJournal of Time Series Econometrics
Volume14
Issue number2
Early online date10 Jan 2022
DOIs
Publication statusPublished - 10 Jan 2022

Bibliographical note

JEL Classification: C13; C32

Funding source: Japan Society for the Promotion of Science Award Identifier / Grant number: 19K01594

Acknowledgments
The authors are most grateful to Yoshihisa Baba, Chialin Chang, Laurent Pauwels, and anonymous two reviewers for very helpful comments and suggestions. The first author acknowledges the financial support of the Japan Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science (JSPS 19K01594), and the Australian Academy of Science. The second author wishes to thank the Australian Research Council and the Ministry of Science and Technology (MOST), Taiwan.

Publisher Copyright:
© 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.

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