Publication Type
Working Paper
Version
publishedVersion
Publication Date
12-2018
Abstract
We propose a factor correlation matrix approach to forecast large covariance matrix of asset returns using high-frequency data. We apply shrinkage method to estimate large correlation matrix and adopt principal component method to model the underlying latent factors. A vector autoregressive model is used to forecast the latent factors and hence the large 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. We conduct Monte Carlo studies to compare the finite sample performance of several methods of forecasting large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors. 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. It also provides higher information ratio for Markowitz portfolios.
Keywords
Large correlation matrix, Nonlinear shrinkage, Dimension reduction, Eigenanalysis, Factor model, High-Frequency data
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
21
Citation
DONG, Yingjie and TSE, Yiu Kuen.
Forecasting large covariance matrix with high-frequency data: A factor correlation matrix approach. (2018). 1-21.
Available at: https://ink.library.smu.edu.sg/soe_research/2270
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.
Comments
Published in Economics Letters, 2020, 195, 109465. DOI: 10.1016/j.econlet.2020.109465