摘要
基于18个经济体760家金融机构SRISK系统性风险数据,采用时变参数向量自回归模型和广义方差分解法测度全球系统性风险的时变传染网络,进而采用分位数Granger因果检验考察各经济体对中国高分位段系统性风险的影响。研究结果表明,结合SRISK数据和时变传染网络能有效识别重大风险事件的发生;发达和新兴经济体均会产生风险净输出效应,且会通过直接输出或香港地区间接输出风险至中国内地;在全样本与2008年金融危机、欧债危机、中美贸易战和新冠疫情重大风险事件期间,中国皆为风险净溢入国且新冠疫情期间溢入效应达历史最高;美英两国在重大风险事件期间均对中国高分位段系统性风险产生显著影响,新冠疫情期间中国高分位段系统性风险遭受外部冲击的形势最为严峻。
This study leveraged systemic risk data from 760 financial institutions across 18 economies to examine the time-varying contagion network of global systemic risk through a time-varying parameter vector autoregression model and generalized variance decomposition.It further assessed the impact of various economies on China’s upper quantile systemic risk by employing the quantile Granger causality test.The findings reveal that integrating systemic risk data with the time-varying contagion network effectively validates the occurrence of major risk events.Both developed and emerging economies act as net risk spreaders,potentially spreading risk to China's Mainland either directly or via the Hong Kong Special Administrative Region.Throughout the sample period—including significant risk events such as the 2008 financial crisis,the European debt crisis,the United States-China trade war,and the COVID-19 pandemic—China was a net risk recipient,with the risk inflow effect intensifying to historic highs during the COVID-19 pandemic.Notably,the United States and United Kingdom exerted substantial impacts on China’s upper quantile systemic risk during these events,with external shocks to China’s upper quantile systemic risk reaching unprecedented severity during the COVID-19 pandemic.
作者
张玉鹏
娄云深
Zhang Yupeng;Lou Yunshe
出处
《金融经济学研究》
北大核心
2024年第3期3-20,共18页
Financial Economics Research
基金
上海市哲学社会科学规划项目(2022ZJB008)
上海市浦江人才计划(21PJC064
22PJC037)。
