DCC-MIDAS 模型综合了 DCC-GARCH 模型与 GARCH-MIDAS 模型的特点， 它可以充分发挥两者的优势。第一，考虑了过去市场信息对关联关系的影响，具 有时变特性；第二，巧妙地将动态相关区分为长期动态相关与短期动态相关，能 够充分揭示金融时间序列间的时变关联关系；第三，无需同频化操作，可以直接 对原始混频数据进行建模，有效地利用了混频数据中的丰富信息。因此， DCC-MIDAS 模型可以改善时变关联关系的估计精度，实现时变协方差矩阵的稳健 与有效估计。为此，本文将 DCC-MIDAS 模型应用于均值-方差组合投资研究，针 对适量金融资产与大规模金融资产两种情形开展实证研究工作。 针对适量金融资产情形，经典均值-方差模型的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，本文提出了一种新 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。 针对大规模金融资产情形，论文提出了一种新的基于 DCC-MIDAS 与范数约 束的时变最小方差模型，简记为 NC-MVP-DCC-MIDAS。该方法利用 DCC-MIDAS 模型，充分挖掘混频数据中所包含的丰富信息，以改进金融资产间动态相关性的 估计。此外，该方法对最小方差优化模型施加弹性网范数约束，以选择合理数量 的金融资产并防止在最终组合投资中出现极端头寸。通过对中国上证 50 指数成分 股的组合投资研究，验证了新模型的有效性。实证结果表明，该方法能有效地解 决高维组合投资选择问题，并且在均值、标准差、Sharpe 比率和组合投资回测等 方面都优于传统的组合投资模型。 本文研究结果表明，DCC-MIDAS 模型在刻画金融资产时变关联关系方面具有 强大的优势，将其引入 Markowitz 组合投资决策模型中，可以显著改善组合投资的 效果。论文实证论证了 DCC-MIDAS 模型在组合投资领域的可行性与优越性，为 组合投资决策分析提供了新的思路与视角，同时这也拓展了 DCC-MIDAS 模型的 应用范围。此外，本文研究结论还可为市场投资者与金融监管机构在风险测度与 风险管理等方面提供一定的决策支持。 关键字，组合投资；DCC-MIDAS；参数化策略；范数约束；弹性网ABSTRACT DCC-MIDAS model combines the characteristics of DCC-GARCH model and GARCH-MIDAS model, which can make full use of their advantages. Firstly, it considers the influence of past market information on the correlation relationship and reflects the nature of time-varying. Secondly, it skillfully distinguishes the dynamic correlation into short-run component and long-run component, which can fully reveal the time-varying correlation among financial time series. Thirdly, it directly models raw mixed frequency data without any frequency conversion, which can effectively exploit rich information contained in mixed frequency data. Therefore, the DCC-MIDAS model can improve the estimation accuracy of time-varying correlation relationship and realize the robust and effective estimation of time-varying covariance matrix. In this regard, the dissertation applies the DCC-MIDAS model to the mean-variance portfolio selection research, and conducts the empirical analysis in the cases of medium- and large-scale financial assets. In the case of medium-scale financial assets, in order to improve the traditional mean-variance model in two aspects including the covariance matrix estimation and static weight processing, the dissertation proposes 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 relationship among financial assets by exploiting high frequency information. In addition, considering the influence of the time-varying characteristics of financial assets on portfolio weights, the dissertation incorporates them to design a parametric weight function, which helps to improve portfolio performance. The dissertation then applies the new model to conduct empirical analysis on several stocks and industry groups in China's stock markets. The research finds 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. In the case of large-scale financial assets, the dissertation proposes a novel norm constrained time-varying minimum variance model with DCC-MIDAS, labeled as NC-MVP-DCC-MIDAS. It applies the DCC-MIDAS model to improve the estimation of dynamic correlations among financial assets by exploiting rich information containedin mixed frequency data. Additionally, it imposes norm constraints on the minimum variance optimization problem with the elastic-net penalty to pick a reasonable number of financial assets and prevent extreme positions in the resulting portfolio. The efficacy of new approach is illustrated via portfolio studies on the constituent stocks in the Shanghai Stock Exchange (SSE) 50 Index of China. The empirical results show that the proposed approach is efficient to solve a large portfolio selection problem and outperforms several competing models in terms of the mean, standard deviation, Sharpe ratio, and portfolio backtesting. To sum up, the DCC-MIDAS model has competitive advantages in describing the time-varying correlation relationship of financial assets. Introducing the DCC-MIDAS model into the Markowitz portfolio selection model can significantly improve portfolio performance. The dissertation empirically demonstrates the feasibility and superiority of DCC-MIDAS model in the field of portfolio selection, which provides a new perspective for the analysis of portfolio selection. At the same time, the dissertation also expands the application scope of the DCC-MIDAS model. In addition, the conclusions of this dissertation can provide decision support for market investors and financial regulators in risk measure and risk management. KEYWORDS: Portfolio; DCC-MIDAS; parametric scheme; norm constraints; elastic-net目 录 第一章 绪论.................................................................................................................... 1 1.1 研究背景及意义 ............................................................................................... 1 1.2 国内外研究综述 ............................................................................................... 2 1.2.1 均值-方差组合投资模型....................................................................... 2 1.2.2 DCC-MIDAS 模型.................................................................................. 4 1.3 研究思路和方法 ............................................................................................... 7 1.3.1 研究思路 ................................................................................................ 7 1.3.2 研究方法 ................................................................................................ 7 1.4 论文主要创新和结构安排 ............................................................................... 8 1.4.1 主要创新 ................................................................................................ 8 1.4.2 结构安排 ................................................................................................ 9 第二章 组合投资决策与波动性建模方法.................................................................. 10 2.1 组合投资决策模型 ......................................................................................... 10 2.1.1 模型表示 .............................................................................................. 10 2.1.2 模型求解 .............................................................................................. 10 2.2 波动性建模 ......................................................................................................11 2.2.1 DCC-GARCH 模型................................................................................11 2.2.2 DCC-MIDAS