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Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence

Guillaume Chevillon (), Alain Hecq () and Sébastien Laurent ()

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Abstract: This paper shows that large dimensional vector autoregressive (VAR) models of fi nite order can generate long memory in the marginalized univariate series. We derive high-level assumptions under which the fi nal equation representation of a VAR(1) leads to univariate fractional white noises and verify the validity of these assumptions for two speci fic 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.

Keywords: Long memory; Vector Autoregressive Model; Marginalization; Final Equation Representation; Volatility (search for similar items in EconPapers)
Date: 2015-05-07
New Economics Papers: this item is included in nep-ets
Note: View the original document on HAL open archive server: https://hal-essec.archives-ouvertes.fr/hal-01158524
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Working Paper: Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence (2015) Downloads
Working Paper: Long memory through marginalization of large systems and hidden cross-section dependence (2015) Downloads
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