51 Pages Posted: 9 Oct 2014 Last revised: 23 Nov 2017
Date Written: November 22, 2017
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
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