极端金融事件对系统性风险的影响分析——以中国银行部门为例

发布时间：2018-11-19 17:25

【摘要】：极端金融事件往往因为发生概率极小而被主流研究所忽略,而且基于平稳随机过程的传统理论和模型难以刻画和分析风险突然全面爆发的现象。本文的边际贡献在于,从理论上将风险激增的非线性机制纳入考量,改进已有模型使之更贴近极端金融事件的实际;从方法上探索利用改进后模型预警系统性风险的可行性并验证其先进性。以中国银行部门为例的研究结果表明,本文提出的跳跃未定权益分析模型将传统模型的连续扩散假设放松为跳跃扩散假设,能更好地刻画极端金融事件的风险激增特征,并可比传统研究提前大约3—6个月预警其系统性风险;而如果进一步纳入本文创造性地综合利用金融市场和宏观经济信息构建的混频宏观动态因子作为风险信息源,就还可提前识别金融市场噪声信号并降低其影响,即使在噪声条件下也能为防范系统性风险提供2—3个月的政策反应时间。
[Abstract]:Extreme financial events are often ignored by the mainstream research because of the minimal probability of occurrence, and the traditional theories and models based on stationary stochastic processes are difficult to describe and analyze the phenomenon that the risks suddenly erupt. The marginal contribution of this paper is that the nonlinear mechanism of risk surge is taken into consideration in theory and the existing model is improved to be closer to the reality of extreme financial events. This paper explores the feasibility of using the improved model to forewarn systemic risk and verifies its advanced nature. Taking the banking sector of China as an example, the results show that the proposed jump contingent equity analysis model can better describe the risk surge characteristics of extreme financial events by relaxing the continuous diffusion hypothesis of the traditional model to the jump diffusion hypothesis. And early warning of systemic risk about 3-6 months ahead of traditional research; If we further integrate the mixed macro dynamic factors constructed by financial market and macroeconomic information as the risk information source, we can recognize the noise signal of financial market in advance and reduce its influence. It can provide 2-3 months policy response time to prevent systemic risk even under noise condition.
【作者单位】：中南财经政法大学金融学院;
【基金】：国家自然科学基金面上项目(71473271) 国家社会科学基金重大项目(16ZDA034)的支持
【分类号】：F832

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