Risk forecast of investment portfolio based on GAS MIDAS Copula model

CAI Guanghui, XU Jun, YING Xuehai

Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (8) : 2030-2044.

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Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (8) : 2030-2044. DOI: 10.12011/SETP2020-2738

Risk forecast of investment portfolio based on GAS MIDAS Copula model

  • CAI Guanghui1, XU Jun1, YING Xuehai2
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Abstract

Considering that the correlation between financial assets has time-varying and long memory, although the MIDAS Copula model incorporating mixed data sampling can characterize time-varying and long memory, its parameter evolution process is relatively simple. Therefore, the generalized autoregressive score (GAS) model is introduced into the MIDAS Copula model as the parameter evolution process, to construct the GAS MIDAS Copula model. The empirical analysis found that the model has improved the ability of the MIDAS Copula model to fit samples; Further select choose three sets of CSI 300 industry indexes with different degrees of correlation, and analyze the model's ability to capture long memory of time-varying correlation coefficients between industries with different degrees of correlation and the risk prediction accuracy of its portfolio. The results showed that: 1) The GAS MIDAS Copula model has the best ability to describe the long memory of the correlation coefficients between highly and moderately related industries; 2) The VaR and ES backtesting results of simple portfolio of three sets of data show that the GAS MIDAS Copula model has the highest prediction accuracy. Finally, various risk prediction results based on different confidence levels, different weight ratios, different rolling window lengths, and different assets confirm the robustness of the GAS MIDAS Copula model.

Key words

long memory / mixed data sampling / generalized autoregressive score / Copula model

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CAI Guanghui , XU Jun , YING Xuehai. Risk forecast of investment portfolio based on GAS MIDAS Copula model. Systems Engineering - Theory & Practice, 2021, 41(8): 2030-2044 https://doi.org/10.12011/SETP2020-2738

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Funding

National Social Science Foundation of China (19BTJ013); 2018 Philosophy and Social Science Planning Project of Zhejiang Province (18NDJC189YB); First Class Discipline of Zhejiang - A (Zhejiang Gongshang University - Statistics) (1020JYN4119004G-94)
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