统计研究 ›› 2019, Vol. 36 ›› Issue (8): 19-31.doi: 10.19343/j.cnki.11-1302/c.2019.08.002

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中国系统性金融风险对宏观经济的影响研究

欧阳资生等   

  • 出版日期:2019-08-25 发布日期:2019-08-25

Research on the Impact of China’s Systemic Financial Risks on the Macroeconomy

Ouyang Zisheng et al.   

  • Online:2019-08-25 Published:2019-08-25

摘要: 基于2007年1月至2017年12月月度数据,本文首先选取金融机构极值风险、金融体系间的传染效应、金融市场的波动性和不稳定性、流动性和信用风险4个层面的14个代表性指标测度了系统性金融风险;然后运用分位数回归度量了单个系统性风险指标对宏观经济的影响;最后运用偏最小二乘分位数回归法构建一个系统性金融风险综合指标进一步实证分析系统性金融风险对宏观经济的影响。研究结果表明:①单个系统性金融风险指数中机构极值风险类别下的指标对宏观经济的影响最大,其中金融体系巨灾风险指数影响效果最显著;②运用偏最小二乘分位数回归构造的系统性金融风险综合指标较之单个系统性金融风险指标,能够更稳健地反映系统性金融风险对宏观经济的影响状况;③从测度效果来看,单个系统性风险指标和系统性金融风险综合指标在下尾分布(0.2分位数)的结果明显优于中间分布(0.5分位数)和上尾分布(0.8分位数)。

关键词: 系统性金融风险, 分位数回归, 偏最小二乘分位数回归, 宏观经济

Abstract: Based on the monthly data from January 2007 to December 2017, this paper first selects 14 representative indicators from four respects—the extreme value risks of financial institutions, the contagion effects between financial systems, the volatility and instability of financial markets, and the liquidity and credit risks of financial markets—to measure the systemic financial risk. Secondly, the quantile regression is used to measure the impact of individual systemic risk indicators on the macro economy. Finally, a partial least squares quantile regression method is used to construct a systemic risk comprehensive indicator and empirically analyze the impact of systemic financial risk on the macro economy. The results show that: ① The indicators under the institutional extreme risk category of the individual systemic financial risk index have the greatest impact on the macro economy, among which the CATFIN has the most significant effect; ② The systemic risk comprehensive index constructed by partial least squares quantile regression can more stably reflect the impact of systemic financial risks on the macro economy than individual systemic risk indicators; ③ From the measurement effect, the individual systemic risk index and the comprehensive systemic risk index in the lower tail distribution (0.2 quantile) are significantly better than the middle distribution (0.5 quantile) and the upper tail distribution (0.8 quantile).

Key words: Systemic Financial Risk, Quantile Regression, Partial Least Squares Quantile Regression, Macroeconomy