Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265014 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Time Series Analysis [ISSN:] 1467-9892 [Volume:] 43 [Issue:] 5 [Publisher:] John Wiley & Sons, Ltd [Place:] Oxford, UK [Year:] 2022 [Pages:] 750-780
Publisher: 
John Wiley & Sons, Ltd, Oxford, UK
Abstract: 
We consider multivariate stationary processes (Xt) satisfying a stochastic recurrence equation of the form Xt=𝕄tXt−1+Qt, where (Qt) are i.i.d. random vectors and 𝕄t=Diag(b1+c1Mt,…,bd+cdMt) are i.i.d. diagonal matrices and (Mt) are i.i.d. random variables. We obtain a full characterization of the vector scaling regular variation properties of (Xt), proving that some coordinates Xt, i and Xt, j are asymptotically independent even though all coordinates rely on the same random input (Mt). We prove the asynchrony of extreme clusters among marginals with different tail indices. Our results are applied to some multivariate autoregressive conditional heteroskedastic (BEKK‐ARCH and CCC‐GARCH) processes and to log‐returns. Angular measure inference shows evidences of asymptotic independence among marginals of diagonal SRE with different tail indices.
Subjects: 
Stochastic recurrence equations
multivariate ARCH
multivariate regular variation
non‐standard regular variation
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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