A blocking and regularization approach to high dimensional realized covariance estimation - Nikolaus Hautsch; Lada M. Kyj; Nikolaus Hautsch (Goethe University Frankfurt, Center for Financial Studies (CFS), 2009)
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the 'RnB' estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results.
urn:nbn:de:hebis:30-72694
Keywords: C14; C22; 330; Covariance Estimation; Blocking; Realized Kernel; Regularization; Microstructure; Asynchronous Trading; Varianzanalyse; Schätztheorie; Core; Multivariate Analyse; Theorie; Schätzung; Börsenkurs; Wertpapierhandel; Aktienmarkt; Mikrostrukturanalyse; USA