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| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | Large dynamic covariance matrices |
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| Institution | University of Zurich |
| Series Name | Working paper series / Department of Economics |
| Number | 231 |
| ISSN | 1664-7041 |
| Number of Pages | 42 |
| Date | 2017 |
| Abstract Text | Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present paper marries these two strands of literature in order to deliver improved estimation of large dynamic covariance matrices. |
| Official URL | http://www.econ.uzh.ch/static/wp/econwp231.pdf |
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| Keywords | Composite likelihood, dynamic conditional correlations, GARCH, Markowitz portfolio selection, nonlinear shrinkage, Portfoliomanagement, Heteroskedastizität, Korrelation, Matrixverfahren |
| Additional Information | Revised version |