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Realized networks
Author(s) -
Brownlees Christian,
Nualart Eulàlia,
Sun Yucheng
Publication year - 2018
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2642
Subject(s) - estimator , covariance , mathematics , estimation of covariance matrices , covariance matrix , inverse , consistency (knowledge bases) , minimum variance unbiased estimator , lasso (programming language) , statistics , econometrics , computer science , discrete mathematics , geometry , world wide web
Summary We introduce LASSO‐type regularization for large‐dimensional realized covariance estimators of log‐prices. The procedure consists of shrinking the off‐diagonal entries of the inverse realized covariance matrix towards zero. This technique produces covariance estimators that are positive definite and with a sparse inverse. We name the estimator realized network, since estimating a sparse inverse realized covariance matrix is equivalent to detecting the partial correlation network structure of the daily log‐prices. The large sample consistency and selection properties of the estimator are established. An application to a panel of US blue chip stocks shows the advantages of the estimator for out‐of‐sample GMV asset allocation.