金融压力指数对宏观经济传导效应研究——基于TVP-SV-VAR模型  

Research on the Transmission Effect of Financial Stress Index on Macroeconomy-Based on TVP-SV-VAR Model

作　　者：郝诗凡 李志民[1] Hao Shifan;Li Zhimin(School of Mathematics-Physics and Finance,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]安徽工程大学数理与金融学院,安徽芜湖241000

出　　处：《洛阳师范学院学报》2025年第2期28-35,共8页Journal of Luoyang Normal University

基　　金：国家自然科学基金面上项目(61873294);安徽高校自然科学研究重大项目(KJ2019ZD16)。

摘　　要：为了对我国系统性金融风险进行更全面的监测,综合考虑更多维度金融风险的影响,将气候风险纳入金融系统的风险监测范畴.首先,基于Python获取气候风险对应的百度搜索指数作为指标;其次,结合CRITIC法与熵权法对各维度金融风险赋予权重,构建我国金融压力指数(CFSI);最后,选取能够代表宏观经济发展的相关指标,建立TVP-SV-VAR模型,探究CFSI与宏观经济的动态传导效应,以验证CFSI的有效性.结果表明,构建的CFSI能显著反映我国金融风险的动态变化.融合多维度影响的CFSI可以更有效地监测系统性金融风险,实现更早的风险干预.Firstly,the Baidu search index corresponding to climate risks is obtained as an indicator by using Python.Secondly,the CRITIC method and the entropy weight method are combined to assign weights to various dimensions of financial risks,constructing China’s Financial Stress Index(CFSI).Finally,indicators representing macroeconomic development are selected to establish a TVP-SV-VAR model to explore the dynamic transmission effects between the CFSI and the macroeconomy,thereby verifying the effectiveness of the CFSI.The results show that the constructed CFSI can significantly reflect the dynamic changes of financial risks in China.The CFSI,which integrates multi-dimensional impacts,can more effectively monitor systemic financial risks,and achieve earlier risk interventions.

关 键 词：系统性金融风险 金融压力指数 气候风险 客观赋权 TVP-SV-VAR模型 

分 类 号：O212[理学—概率论与数理统计] F830[理学—数学]