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美国不确定性冲击对全球股市波动的影响研究

A Study on the Impact of US Uncertainty Shocks on Global Stock Market Volatility
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摘要 选取2000—2021年美国经济、金融、经济政策和地缘风险四类不确定性指数以及全球GDP排行前15国家股市收益率数据,基于多维不确定性冲击框架,运用单因子、双因子和多因子混频波动率GARCH-MIDAS模型,从样本内拟合与样本外预测两个方面实证考察美国不确定性冲击对全球主要国家股市波动的差异化影响。研究表明:美国经济不确定性和金融不确定性对多数国家股市长期波动均有正向推动作用,其中,美国金融不确定性的影响最为广泛;美国金融不确定性是影响中国股市长期波动的主要因素,且中美贸易摩擦主要通过美国金融不确定性传导;美国经济政策不确定性上升会增加俄罗斯和墨西哥股市长期波动,美国地缘风险对意大利股市长期波动存在显著正向影响。 Selecting four types of uncertainty indexes of macroeconomic uncertainty(MU),financial uncertainty(FU),economic policy uncertainty(EPU)and geopolitical risk(GPR)in the US from 2000 to 2021,as well as the stock returns data of the top 15 countries in the global GDP ranking,and based on the multi-dimensional uncertainty shock framework,single-factor,double-factor and multi-factor mixed frequency GARCH-MIDAS models are used to empirically investigate the differentiated impact of US uncertainty on the stock market volatility of major global countries from two aspects:in-sample fitting and out-of-sample forecasting.The results show that:(1)US financial uncertainty and macroeconomic uncertainty have a positive impact on the long-term stock market volatility in most countries,among which US financial uncertainty has the most extensive impact.(2)The US financial uncertainty is the main factor affecting the long-term volatility of China’s stock market,and the Sino-US trade friction is mainly transmitted through the US financial uncertainty.(3)The increase of US economic policy uncertainty will increase the long-term volatility of Russian and Mexican stock markets,and the US geopolitical risk has a significantly positive impact on the long-term volatility of Italian stock market.
作者 李政 李薇 LI Zheng;LI Wei(School of Finance,Tianjin University of Finance and Economics,Tianjin300222,China)
出处 《财经理论与实践》 北大核心 2024年第2期48-55,共8页 The Theory and Practice of Finance and Economics
基金 国家社科基金重大项目(22&ZD120) 教育部人文社会科学重点研究基地重大项目(22JJD790046) 教育部人文社会科学研究一般项目(17YJA790024)。
关键词 美国不确定性 股市波动 GARCH-MIDAS 样本外预测 US uncertainty stock market volatility GARCH-MIDAS out-of-sample forecasting
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