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

Yingjie Dong and Yiu-Kuen Tse

Economics Letters, 2020, vol. 195, issue C

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: Large correlation matrix; Nonlinear shrinkage; Dimension reduction; Eigenanalysis; Factor model; High-frequency data (search for similar items in EconPapers)
JEL-codes: C41 G12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:195:y:2020:i:c:s016517652030286x

DOI: 10.1016/j.econlet.2020.109465

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