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Copula-GARCH方法的投资组合VaR分析
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Abstract:
在进行金融资产的组合风险分析时，描述多个金融资产之间的相关结构成为确定最优组合权重的关键因素之一。在定量研究中，准确刻画金融资产之间的非对称尾部相关结构尤为关键。文章基于沪深300指数和黄金Au9999的日对数收益率数据，采用Copula-GARCH模型及蒙特卡洛模拟法对沪深300指数和黄金Au9999的日对数收益率进行实证分析。研究结论显示：1) 对模型进行参数估计及检验之下，结果显示t-Copula模型对于两类资产的相关关系拟合优度最高；2) 使用蒙特卡洛模拟法对组合的风险进行估算，当沪深300和黄金Au9999投资比例为0.3:0.7时，在险价值VaR最小，能够在极端情况下最大程度减少损失规避风险。
In the analysis of portfolio risk for financial assets, describing the correlation structure among multiple financial assets becomes one of the key factors in determining the optimal portfolio weights. In quantitative research, accurately characterizing the asymmetric tail dependencies among financial assets is particularly crucial. The article conducts empirical analysis based on the daily logarithmic returns of the CSI 300 Index and gold Au9999, employing the Copula-GARCH model and Monte Carlo simulation method. The research findings indicate: 1) Under parameter estimation and testing, the t-Copula model demonstrates the highest goodness-of-fit for the correlation between the two asset classes. 2) Using the Monte Carlo simulation method to estimate portfolio risk, when the investment ratio of CSI 300 and gold Au9999 is 0.3:0.7, the Value at Risk (VaR) is minimized, effectively reducing losses and mitigating risk in extreme scenarios.
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