Large-dimensional covariance matrix estimation and its application with RCM using high frequency data

NI Xuanming, QIAN Long, ZHAO Huimin, HUANG Song

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (8) : 1943-1953.

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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (8) : 1943-1953. DOI: 10.12011/1000-6788-2019-0301-11

Large-dimensional covariance matrix estimation and its application with RCM using high frequency data

  • NI Xuanming1, QIAN Long1, ZHAO Huimin2, HUANG Song1
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Abstract

Factor-based covariance estimation is an important direction of high-frequency and large-dimensional covariance estimation. In order to overcome the subjectivity of sector-based block-diagonalizing method, we introduce RCM reordering method to reorder the residual matrix and conduct thresholding under new block-diagonal structure. Firstly, we disorganize the original residual matrix that contains clear block-diagonal structure. Then we use RCM to restore its order and the result shows that RCM can fully restore the latent block-diagonal structure. Next, using high-frequency data of market crash period in 2015 and year 2018, we combine pre-averaging method and RCM-based POET to construct a new covariance estimator. The comparison of this new estimator against the others suggests that the modified factor model and estimator outperform other covariance estimators in terms of predicting the future. Moreover, it also performs well when optimizing the minimum variance portfolio with gross-exposure constraint.

Key words

RCM algorithm / factor model / larger-dimensional covariance matrix / principle components analysis (PCA)

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NI Xuanming , QIAN Long , ZHAO Huimin , HUANG Song. Large-dimensional covariance matrix estimation and its application with RCM using high frequency data. Systems Engineering - Theory & Practice, 2019, 39(8): 1943-1953 https://doi.org/10.12011/1000-6788-2019-0301-11

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Funding

National Natural Science Foundation of China (71721001)
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