Long memory through marginalization of large systems and hidden cross-section dependence

Guillaume Chevillon, Alain Hecq and Sébastien Laurent

No 14, Research Memorandum from Maastricht University, Graduate School of Business and Economics (GSBE)

Abstract: This paper shows that large dimensional vector autoregressive (VAR) models of finite order can generate long memory in the marginalized univariate series. We derive high-level assumptions under which the final equation representation of a VAR(1) leads to univariate fractional white noises and verify the validity of these assumptions for two specific models. We consider the implications of our findings for the variances of asset returns where the so-called golden-rule of realized variances states that they tend always to exhibit fractional integration of a degree close to 0:4.

Date: 2015-01-01
New Economics Papers: this item is included in nep-ets and nep-ore
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence (2015)
Working Paper: Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:unm:umagsb:2015014

DOI: 10.26481/umagsb.2015014

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