Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence
Guillaume Chevillon (),
Alain Hecq () and
Sébastien Laurent ()
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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)
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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:hal:wpaper:hal-01158524
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