A blocking and regularization approach to high dimensional realized covariance estimation
- 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.
| Author: | Nikolaus Hautsch, Lada M. Kyj, Roel C. A. Oomen |
|---|---|
| URN: | urn:nbn:de:hebis:30-72694 |
| Parent Title (German): | Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2009,20 |
| Series (Serial Number): | CFS working paper series (2009, 20) |
| Document Type: | Working Paper |
| Language: | English |
| Year of Completion: | 2009 |
| Year of first Publication: | 2009 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2009/12/02 |
| Tag: | Asynchronous Trading; Blocking; Covariance Estimation; Microstructure; Realized Kernel; Regularization |
| GND Keyword: | Kovarianzanalyse; Schätzfunktion |
| HeBIS-PPN: | 220153930 |
| Institutes: | Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS) |
| Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
| JEL-Classification: | C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C14 Semiparametric and Nonparametric Methods |
| C Mathematical and Quantitative Methods / C2 Single Equation Models; Single Variables / C22 Time-Series Models; Dynamic Quantile Regressions (Updated!) | |
| Licence (German): |  Deutsches Urheberrecht |
