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

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

No WP1507, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School

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)
JEL-codes: C10 C32 C58 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2015-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

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Related works:
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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