Portfolio optimization based on realized semi-covariance

QIAN Long, PENG Fangping, SHEN Xinyuan, SUN Xiaoxia

Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (1) : 34-44.

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Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (1) : 34-44. DOI: 10.12011/SETP2020-1029

Portfolio optimization based on realized semi-covariance

  • QIAN Long1, PENG Fangping2, SHEN Xinyuan3, SUN Xiaoxia4
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Abstract

Among traditional volatility measurements, normal covariance estimators are not able to distinguish the downside risk and upside gains of asset return, while traditional lower partial moment estimators are asymmetric and impossible to sum up. Therefore, this paper introduces a new risk measurement called realized semi-covariance (RSCOV) to conduct volatility forecasting and portfolio optimization. Based on decomposition of realized covariance matrix, we test it on two common diversification investing strategies, equally-weighted risk contribution (ERC) strategy and global minimum variance (GMV) strategy. To perform forecasting, we adopt online weighted ensemble (OWE) algorithm in machine learning domain to boost the out-of-sample performance of HAR-RV. Compared to existing covariance or realized covariance, we find that realized downside semi-covariance matrix, that only contains information about negative volatility, can be used to better balance the risk contribution of assets in portfolio. Then, using high-frequency data of A share market spanning from 2011 to 2018, empirical result shows that our OWE-HAR-RV can outperform HAR-RV in monthly prediction. Lower RSCOV can be applied to ensure risk parity and minimum variance portfolio strategies to achieve better allocated asset weights and lower maximum loss while maintaining certain portfolio return.

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portfolio optimization / volatility forecasting / realized semi-covariance / online weighted ensemble algorithm

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QIAN Long , PENG Fangping , SHEN Xinyuan , SUN Xiaoxia. Portfolio optimization based on realized semi-covariance. Systems Engineering - Theory & Practice, 2021, 41(1): 34-44 https://doi.org/10.12011/SETP2020-1029

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Funding

National Natural Science Foundation of China (71673312)
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