FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA
By Minjing Tao, Yazhen Wang, Xiaohong Chen. Econometric Theory, 2013
52 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/41f52c288852c23bf06ec5f81bc13ebd24a71ebf
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
- Kevin Sheppard
- Yacine Aït-Sahalia
- D. Xiu
- Asger Lunde
- N. Shephard
- Tim Bollerslev
- Andrew J. Patton
- V. Todorov
In Finance
- Kevin Sheppard
- Asger Lunde
- Yacine Aït-Sahalia
- D. Xiu
- N. Shephard
- Tim Bollerslev
- Andrew J. Patton
- V. Todorov
Citations in the corpus
- Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, background): “See also Wang and Zou (2010) and Tao, Wang, and Chen (2013) who worked with large dimensional covariances and high-frequency data using sparsity to regularize their applications.”
- Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, background): “Dealing with an increased dimensionality without a factor structure typically requires the additional assumption that the population covariance matrix itself is sparse (see, e.g., Tao et al. (2011), Tao et al. (2013b), and Tao et al. (2013a)).”