Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2020
Abstract
We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors than some selected competitors. Empirical application to a portfolio of 100 NYSE and NASDAQ stocks shows that our method provides lower out-of-sample realized variance in selecting global minimum variance portfolio
Keywords
Dimension reduction, Eigenanalysis, Factor model, High-frequency data, Large correlation matrix, Nonlinear shrinkage
Discipline
Econometrics
Research Areas
Econometrics
Publication
Economics Letters
Volume
195
First Page
1
Last Page
4
ISSN
0165-1765
Identifier
10.1016/j.econlet.2020.109465
Publisher
Elsevier
Embargo Period
5-14-2021
Citation
DONG, Yingjie and TSE, Yiu Kuen.
Forecasting large covariance matrix with high-frequency data: A factor approach for the correlation matrix. (2020). Economics Letters. 195, 1-4.
Available at: https://ink.library.smu.edu.sg/soe_research/2473
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.econlet.2020.109465