中国信贷波动对金融系统性风险影响的实证研究

Empirical Analysis of the Impact of Chinese Credit Volatility on Financial Systemic Risk

本文基于1991-2013年年度数据,通过构建风险预警模型对我国金融系统性风险进行测度,并运用Beveridge-Nelson数据处理技术对信贷规模进行分解,提取周期成分和随机趋势成分,在此基础上进行实证分析。结果发现:(1)我国金融系统性风险与信贷周期成分呈反向变动关系,而与随机趋势成分呈同向变动关系;(2)信贷波动对金融系统性风险的影响主要来源于信贷随机成分的波动,信贷扩张会显著提升金融系统性风险。

Based on the annual data from 1991 to 2013, this paper establishes the risk early warning model to measure China＇s financial systemic risk, and uses Beveridge-Nelson data processing technique to decompose credit scale in order to extract business cycle component and stochastic trend component, then conducts empirical analysis. It is found that： （1） Financial systemic risk changes in negative relationship with credit business cycle component, but in positive relationship with stochastic trend component; （2） The impact of credit volatility on financial systemic risk mainly comes from credit stochastic trend component, and credit expansion will significantly enhance the financial systemic risk.