Realized Networks

51 Pages Posted: 9 Oct 2014 Last revised: 3 Feb 2018

See all articles by Christian T. Brownlees

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences

Eulalia Nualart

Universitat Pompeu Fabra - Department of Economics and Business; Barcelona Graduate School of Economics (Barcelona GSE)

Yucheng Sun

Capital University of Economics and Business

Date Written: January 31, 2018

Abstract

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 bluechips shows the advantages of the estimator for out-of-sample GMV asset allocation.

Keywords: Networks, Realized Covariance, Lasso

JEL Classification: C13, C33, C52, C58

Suggested Citation

Brownlees, Christian T. and Nualart, Eulalia and Sun, Yucheng, Realized Networks (January 31, 2018). Available at SSRN: https://ssrn.com/abstract=2506703 or http://dx.doi.org/10.2139/ssrn.2506703

Christian T. Brownlees (Contact Author)

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain

HOME PAGE: http://econ.upf.edu/~cbrownlees/

Eulalia Nualart

Universitat Pompeu Fabra - Department of Economics and Business ( email )

Barcelona
Spain

HOME PAGE: http://www.nualart.es

Barcelona Graduate School of Economics (Barcelona GSE) ( email )

Ramon Trias Fargas, 25-27
Barcelona, Barcelona 08005
Spain

Yucheng Sun

Capital University of Economics and Business ( email )

Beijing
China

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