Large Dynamic Covariance Matrices
By Robert F. Engle, Olivier Ledoit, Michael Wolf. 2019
239 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/b16a452ff8340a4eb96b0e1aa5931427d736cd8a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Clifford Lam
- Michael Wolf
- Olivier Ledoit
- Raj Agrawal
- Caroline Uhler
- Uma Roy
- Victor DeMiguel
- Francisco Javier Nogales
In Finance
- Clifford Lam
- Michael Wolf
- Olivier Ledoit
- Raj Agrawal
- Caroline Uhler
- Uma Roy
- Victor DeMiguel
- Francisco Javier Nogales
Citations in the corpus
- High‐dimensional covariance matrix estimation (2019, methodology): “Partial solutions are offered in (cid:12)nance, for example, in Engle et al. (2019) where they proposed a large dynamic covariance matrix estimator, but a useful estimator may be different in other scienti(cid:12)c (cid:12)elds.”
- Quadratic Shrinkage for Large Covariance Matrices (2020, background): “The main differences between Stein’s notation and ours are that the roles of the indices i and j are swapped, and his eigenvalues j ( j = 1, . . . ,p) are equal to n times those of the sample covariance matrix. The resulting covariance matrix estimator is S̃n := ∑p i=1 d̃n,i · un,iu ′ n,i . Although”
- Covariance Matrix Estimation under Total Positivity for Portfolio Selection* (2019, methodology): “…while such estimators are optimal in a minimax sense for the Frobenius and spectral norm loss, these losses may not be relevant to measure the excess risk that results from using an estimate of Σ ∗ instead of Σ ∗ itself to compute the Markovitz portfolio; see [ELW17, Section 4.1] for details.”
- Interconnectedness in the global financial market (2017, unknown): “While advances have been made in applying these models to larger data sets they are still impractical for cases like ours (see also Aielli, 2013; Engle et al., 2019; Greenwood-Nimmo et al., 2015).”
- Analytical nonlinear shrinkage of large-dimensional covariance matrices (2020, unknown): “The Matlab code for NERCOME was generously provided by Professor Clifford Lam from the Department of Statistics at the London School of Economics. The code for the QuEST package comes from the numerical implementation detailed in Ledoit and Wolf (2017b) and is freely downloadable from the academic w”