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Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models

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  • Han, Yingying
  • Gong, Pu
  • Zhou, Xiang

Abstract

In this paper, we apply time varying Gaussian and SJC copula models to study the correlations and risk contagion between mixed assets: financial (stock), real estate and commodity (gold) assets in China firstly. Then we study the dynamic mixed-asset portfolio risk through VaR measurement based on the correlations computed by the time varying copulas. This dynamic VaR-copula measurement analysis has never been used on mixed-asset portfolios. The results show the time varying estimations fit much better than the static models, not only for the correlations and risk contagion based on time varying copulas, but also for the VaR-copula measurement. The time varying VaR-SJC copula models are more accurate than VaR-Gaussian copula models when measuring more risky portfolios with higher confidence levels. The major findings suggest that real estate and gold play a role on portfolio risk diversification and there exist risk contagion and flight to quality between mixed-assets when extreme cases happen, but if we take different mixed-asset portfolio strategies with the varying of time and environment, the portfolio risk will be reduced.

Suggested Citation

  • Han, Yingying & Gong, Pu & Zhou, Xiang, 2016. "Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 940-953.
    • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:940-953
    DOI: 10.1016/j.physa.2015.10.088
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    Cited by:

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    2. Herteliu, Claudiu & Levantesi, Susanna & Rotundo, Giulia, 2021. "Network analysis of pension funds investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    3. Luo, Changqing & Liu, Lan & Wang, Da, 2021. "Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Chunyi Lu & Zhuoqi Teng & Yu Gao & Renhong Wu & Md. Alamgir Hossain & Yuantao Fang, 2022. "Analysis of Early Warning of RMB Exchange Rate Fluctuation and Value at Risk Measurement Based on Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1501-1524, April.
    5. Sui, Xin & Li, Liang, 2018. "Guarantee network model and risk contagion," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 323-329.
    6. Sui, Xin & Li, Liang & Chen, Xiaohui, 2020. "Risk contagion caused by interactions between credit and guarantee networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    7. Cerqueti, Roy & Giacalone, Massimiliano & Panarello, Demetrio, 2019. "A Generalized Error Distribution Copula-based method for portfolios risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 687-695.
    8. Bai, Lan & Zhang, Xuhui & Liu, Yuntong & Wang, Qian, 2019. "Economic risk contagion among major economies: New evidence from EPU spillover analysis in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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