基于DCC-MIDAS与参数化策略的时变组合投资决策
Time-Varying Portfolio Selection Based on DCC-MIDAS and Parametric Scheme
在经典的均值-方差模型中, 组合投资效果往往受到协方差矩阵估计精度低与权重静态设置两个方面的不利影响. 为此, 文章提出了一种新的时变组合投资决策模型: 一方面引入DCC-MIDAS模型, 运用高频信息, 提高金融资产间的动态关联关系估计精度; 另一方面考虑金融资产时变特征对组合投资权重的影响, 进行参数化设计, 改善时变组合投资效果. 对中国股市的个股以及行业板块进行了实证研究, 结果表明: 账面市值比、市盈率与组合投资权重呈正相关关系, 市值与组合投资权重呈负相关关系; 新模型在标准差风险、Sharpe比率和有效前沿等方面, 都优于传统的组合投资模型.
In order to improve the traditional mean-variance model in two aspects including the covariance matrix estimation and static weight processing, we propose a new time-varying portfolio selection model based on the DCC-MIDAS model and a parametric scheme. Specifically, the DCC-MIDAS model is introduced to improve the prediction accuracy of dynamic association relationships among financial assets by exploiting high frequency information. In addition, considering the influence of the time-varying characteristics of financial assets on portfolio weights, we incorporate them to design a parametric weight function, which helps to improve portfolio performance. Then, we apply the new model to conduct empirical analysis on several single stocks and industry groups in China's stock markets. We find that book-to-market ratio (BTM) and price earnings ratio (PE) are positively correlated with portfolio weights, while market equity (ME) presents negatively. The empirical results show that the proposed model outperforms several competing models in terms of standard deviation risk, Sharpe Ratio, and efficient frontier.
组合投资 / 均值-方差模型 / / DCC-MIDAS / 参数化策略. {{custom_keyword}} /
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