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我国股票市场与房地产市场于数十年间呈现出快速发展态势，其间的风险与冲击对整体经济的稳定性及发展进程产生了极为深远的影响。现阶段，针对我国股票市场与房地产市场之间的风险溢出以及动态冲击开展深入分析，更成为极具现实紧迫性的实践问题。随着全球化进程的持续加速以及金融市场开放程度的日益提高，国际金融市场的不确定性显著增强，其对中国市场的影响愈发突显，对我国金融市场稳定运行带来诸多挑战。维护市场间的协调共生关系，对于保障经济平稳健康发展至关重要。在当前市场环境之下，深入探究股票市场与房地产市场之间的双向风险溢出效应及动态冲击效应意义重大。一方面，有助于精准把握两个市场的内在关联逻辑，全面剖析其对中国经济的综合影响；另一方面，能够为政策制定者提供科学、可靠的决策依据，助力其制定更为精准、高效的宏观调控政策，对深化金融市场运行规律认知、推动学术理论发展具有不可忽视的重要意义。

本文参考国内外已有的文献研究，结合新时代下中国金融体系的特点，研究股票市场与房地产市场间风险溢出及动态冲击关系。从中国证监会发布的各项文件中选取涵盖我国股票市场的六种股票指数作为股票市场的宏观经济变量，从供给侧和价格方面选取2种指标代表我国房地产市场分别从宏观和微观角度进行分析。本文首先对两市场间的关联性进行了granger因果检验和谱分析，对市场间的因果关系和周期性进行分析。其次，基于时变Copula-CoVAR模型研究股票市场与房地产市场双向风险溢出效应。利用软件确认最优时变Copula函数，再通过K-S检验结果选取倾斜t分布，进一步计算样本周期内房地产市场和股票市场的VaR和CoVaR，基于上述计算结果选择相对风险溢出量（%CoVaR）作为风险溢出强度的衡量指标，最终对得出的风险溢出效应实证结果进行描述分析。最后，基于TVP-SV-VAR模型对股票市场与房地产市场的动态冲击效应进行分析。通过脉冲响应函数结果，结合谱分析从不同滞后期和不同时点两个视角出发，对样本期内中国股票市场与房地产市场的动态冲击效应进行深入研究。

研究结果表明：第一，本文所做市场关联性分析研究可以得出显著双向因果关系的结论。Granger因果检验结果显示各指标互为因果，证实经济变量相互影响显著，展现出紧密联动性。同时，从周期特征看市场指征变量数据均呈周期性波动，主周期长度相近且同步性高，长周期波动相关性强且具有时滞性和关联性。第二，本文所做风险溢出研究可以看出时变t Copula优势明显，相对风险溢出测量方法更优。房地产市场风险溢出针对不同股票价格指数的单向强度有显著不同，而不同股票价格指数对房地产市场指标的单向风险溢出强度的变化趋于一致，且后者强于前者具有不对称性。第三，本文在对市场的动态冲击效应分析研究中，可以看出存在复杂动态的相互影响关系，受多种因素综合作用。在不同滞后期时，短期冲击作用更明显。在不同时点分析时，在不同阶段呈现不同特征和趋势，投资者和政策制定者需充分考虑这些因素以应对市场变化和制定合理决策。

China's stock market and real estate market have shown rapid development over the past decades, and the risks and shocks have had a profound impact on the stability and development of the overall economy. At this stage, in-depth analysis of the risk spillovers and dynamic shocks between China's stock market and real estate market has become a practical issue of great practical urgency. As the process of globalization continues to accelerate and the degree of openness of the financial market increases, the uncertainty of the international financial market has increased significantly, and its impact on the Chinese market has become more and more prominent, bringing many challenges to the stable operation of China's financial market. Maintaining a coordinated and symbiotic relationship between markets is crucial to ensuring the stable and healthy development of the economy. Under the current market environment, it is of great significance to explore the two-way risk spillover effect and dynamic impact effect between the stock market and real estate market. On the one hand, it helps to accurately grasp the intrinsic logic of the two markets and comprehensively analyze their comprehensive impact on China's economy; on the other hand, it can provide policymakers with scientific and reliable decision-making basis and help them formulate more accurate and efficient macroeconomic control policies, which is of great significance in deepening the cognition of the operation law of the financial market and promoting the development of academic theories.

This paper studies the risk spillover and dynamic impact relationship between the stock market and the real estate market with reference to the existing literature at home and abroad, combining with the characteristics of China's financial system in the new era. Six stock indices covering China's stock market are selected from the documents issued by China Securities Regulatory Commission as the macroeconomic variables of the stock market, and two kinds of indexes are selected from the supply side and the price side to represent China's real estate market to be analyzed from the macro and micro perspectives respectively. In this paper, firstly, granger causality test and spectral analysis are carried out to analyze the correlation between the two markets, and the causality and cyclicality between the markets are analyzed. Secondly, the two-way risk spillover effect between stock market and real estate market is studied based on the time-varying Copula-CoVAR model. The software is used to confirm the optimal time-varying Copula function, and then the skewed t-distribution is selected through the K-S test results, the VaR and CoVaR of the real estate market and the stock market in the sample period are further calculated, and based on the results of the above calculations, the relative risk spillover (%CoVaR) is selected as a measure of the intensity of risk spillover, and the resulting empirical results of the risk spillover effect are finally analyzed in a descriptive manner. Finally, the dynamic shock effects of the stock market and real estate market are analyzed based on the TVP-SV-VAR model. Through the impulse response function results, combined with the spectral analysis from the perspective of different lags and different time points, the dynamic shock effects of the Chinese stock market and real estate market during the sample period are studied in depth.

The results of the study show that, firstly, the market correlation analysis conducted in this paper can conclude that there is a significant bi-directional causality, and the results of the Granger causality test show that the indicators are causally related to each other, which confirms that the economic variables have significant influence on each other and show a close linkage. At the same time, from the cyclical characteristics of the market indicator variables data are cyclical fluctuations, the length of the main cycle is similar and high synchronization, the correlation of long-cycle fluctuations is strong and has a time lag and correlation. Second, the risk spillover research done in this paper can be seen in the time-varying t Copula advantage is obvious, and the relative risk spillover measurement method is better. Real estate market risk spillover against different stock price indexes have significantly different one-way strength, while different stock price indexes on the real estate market indicators of the one-way risk spillover strength of the change tends to be consistent, and the latter is stronger than the former has an asymmetry. Third, in this paper, in analyzing and studying the dynamic shock effects on the market, it can be seen that there is a complex and dynamic interplay relationship, which is affected by a combination of factors. At different lags, the role of short-term shocks is more pronounced. When analyzed at different points in time, different characteristics and trends are presented at different stages, and investors and policymakers need to fully consider these factors in order to cope with market changes and make reasonable decisions.