Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice
By Asger Lunde, N. Shephard, Kevin Sheppard. 2016
76 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f81cd09e2017b77a72543dc1917c9d41003714a5
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In Economics and Econometrics
- Michael Wolf
- Olivier Ledoit
- G. D. Nard
- Victor DeMiguel
- Francisco Javier Nogales
- Jiahan Li
- Raman Uppal
- Jianqing Fan
In Finance
- Michael Wolf
- Olivier Ledoit
- G. D. Nard
- Victor DeMiguel
- Francisco Javier Nogales
- Jiahan Li
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- Raman Uppal
In Statistics and Probability
- Michael Wolf
- Olivier Ledoit
- Clifford Lam
- G. D. Nard
- Noureddine El Karoui
- Fabien Lotte
- Benjamin Blankertz
- Victor DeMiguel
Citations in the corpus
- Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice (2017, unknown): “By realized measures of a covariance matrix we mean estimates of the integrated covariance matrix based on ultra-high-frequency data as, for example, the composite realized kernel of Lunde et al. (2015).”
- Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, methodology): “performance of portfolio managers, to assess return anomalies, to predict returns, and to construct portfolios; for example, see Meucci (2005) and Chincarini and Kim (2006).”