European Business Schools Librarian's Group
ESSEC Working Papers,
ESSEC Research Center, ESSEC Business School
No WP1507: Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence
Guillaume Chevillon (), Alain Hecq () and Sébastien Laurent ()
Additional contact information
Guillaume Chevillon: ESSEC Business School, Postal: AVENUE BERNARD HIRSCH, CS 50105 CERGY, 95021 CERGY PONTOISE CEDEX, FRANCE
Alain Hecq: Maastricht University (Department of Quantitative Economics), Postal: Maastricht University, Department of Quantitative Economics, School of Business and Economics, (room A2.21), P.O. Box 616, 6200 MD Maastricht, THE NETHERLANDS,
Sébastien Laurent: Aix-Marseille University (Aix-Marseille School of Economics), Postal: GREQAM , Château La Farge , Route des Milles , 13290 Les Milles, FRANCE
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
31 pages, June 2015
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