Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/43197 
Erscheinungsjahr: 
2009
Schriftenreihe/Nr.: 
CFS Working Paper No. 2009/20
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Covariance Estimation
Blocking
Realized Kernel
Regularization
Microstructure
Asynchronous Trading
JEL: 
C14
C22
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
319.26 kB





Publikationen in EconStor sind urheberrechtlich geschützt.