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Portfolio Research Based on Mean-Realized Variance-CVaR and Random Matrix Theory under High-Frequency Data

In this paper, random matrix theory is employed to perform information selection and denoising, and mean-realized variance-CVaR multi-objective portfolio models before (after) denoising are constructed for high-frequency data. The empirical study is conducted based on high-frequency data from stocks in the SSE 180 Index. Compared with the existing literatures, the main contribution of this paper is the introduction of both realized covariance matrix and random matrix theory in multi-objective portfolio problem. The result shows that the use of the realized covariance matrix can reduce the loss of market information, and random matrix theory could help improve the quality of information contained in correlation matrix among assets. Under the denoised mean-realized variance-CVaR criterion, the new portfolio selection has better out-of-sample performance.