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

G. Chevillon, A.W. Hecq, S.F.J.A. Laurent

Research output: Working paper / PreprintWorking paper

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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.
Original languageEnglish
Place of PublicationMaastricht
PublisherMaastricht University, Graduate School of Business and Economics
Publication statusPublished - 1 Jan 2015

Publication series

SeriesGSBE Research Memoranda

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