中文摘要：

我国股市个股价格同时上涨或同时下跌的联动现象极为普遍，传统上使用向量自回归、协整、有向非循环图等方法主要用于少量股票或市场之间的联动性研究，不适于直接对大规模个股之间的联动关系进行研究。文章关注大规模时序图模型结构建立及估计方法，通过将ADL方法引入SPACE算法，提出了可以估计高维低样时序图模型的ADL．SPACE算法；设计模拟实验考察了算法中惩罚参数A值的设置对于节点自回归相关性捕获的有效性；在实证研究中，文章使用了ADL—SPACE算法对个股联动研究了三方面的内容：1．基于个股联动的代表性行业之间的联动性；2．设计了我国A股市场中行业联动强度，对行业内外联动性进行综合评价和分析；3．采用一阶滞后个股基于时序图模型结果构造了投资组合，模拟显示收益预期表现良好。以上研究均表明时序SPACE图模型方法在大规模股票的联动探测中有较好的应用前景。

英文摘要：

Co-movement which Stock prices rise together or fall together is popular in Chinese stock market. Classical methods, such as VAR, Co-integration, DAG, are limited when they are directly applied to co-movement analysis with huge stocks. In this paper, we focus on building graphical structure and estimating for analyzing co-movement with high dimensional small sample time series. By introducing ADL into SPACE model, we propose ADL-SPACE that can estimate high-dimensional-time-series Graphical model. We design simulation experiment to explore the efficiency of ADL-SPACE algorithm in different penalty A setting. In the demonstration research, this paper apply ADL-SPACE algorithm to construct strengthen index to rank the strength of Co-movement with industry plates in China＇s stock market. We construct a good strength index Portfolio based on the result from the ADL-SPACE model which prove ADL-SPACE algorithm is useful in practice.