Forecasting large covariance matrix with high-frequency data: A factor approach for the correlation matrix

• Main Authors: DONG, Yingjie, TSE, Yiu Kuen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Dimension reduction; Eigenanalysis; Factor model; High-frequency data; Large correlation matrix; Nonlinear shrinkage; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2473; https://ink.library.smu.edu.sg/context/soe_research/article/3472/viewcontent/Forecasting_large_covar_matrix_2020_av.pdf
• Institution: Singapore Management University

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