系统性金融风险的联合网络关联度测量及频率研究——基于局部平稳的非参数时变VHAR模型

Research on Frequency of the Joint Network Connectedness of Systemic Financial Risks in China--Based on the Locally Stationary Non-parametric Time-varying Vector HAR Model

准确度量系统性金融风险并分析其来源是研究系统性金融风险的重要问题,也是制定风险防范措施的必要保证。本文以金融类股票的高频数据为对象,通过假设向量异质自回归模型(VHAR模型)的参数为时间t/T的函数,建立了高维度局部平稳的非参数时变VHAR模型(即tv-VHAR model),并利用拟贝叶斯局部似然(QBLL)估计方法,解决了“维数灾难”下的估计问题。进一步,为更准确度量系统性金融风险,对Baruník和K?ehlík(2018)模型存在的缺陷进行修正,提出了联合网络关联度的频率成分指标,结合2015年股灾和新冠疫情这两次危机事件分析了我国系统性金融风险。结果表明:(1)我国金融系统风险的联合网络关联度比较高且持续性波动。(2)从频率角度来看,正常时期短期成分在风险网络中占据主导地位,其次为中期,最后为长期。(3)危机时期,短期成分迅速下降,而中、长期成分迅速上升,甚至超过了短期成分。(4)相比新冠疫情危机事件,股灾危机对投资者信心影响更大,导致危机持续时间更长。(5)各金融机构在风险网络中的作用差异较大:大型证券公司、股份制银行主要表现为风险净传播者,而大型国有商业银行及其他金融机构主要表现为风险净接受者。

In the past decade,China has experienced two critical events-the 2015 stock market disaster and the coronavirus disease 2019(Covid-19),which have had a great impact on the financial markets.Through the comparison of the two crises,it is found that the impact of the stock market disaster on financial markets is much stronger and longer than the coronavirus disease 2019,although the financial markets experienced sharp declines in both crises.It matters to both governments and academia to find out the reasons behind the differ‐ences in the causes of the two crises to financial risks and further figure out the sources of systemic risks.Taking the high-frequency data of financial stocks as object,a locally stationary non-parametric time-varying vector HAR model(tv-VHAR model)under high dimensions is constructed firstly in this article by assuming that the parameters of the vector Heterogeneous Autoregression Model(HAR model)are functions of time t/T.On this basis,the estimation problem under the Curse of dimensionality is solved by applying the Quasi-Bayesian Local Likelihood methods to the tv-VHAR model.Secondly,the frequency component of the joint connected‐ness is proposed in this article to increase the measurement accuracy of the systemic financial risks by revising the Baruník and Křehlík(2018)model.Finally,the systemic financial risks in China are systematically analyzed and is proved to have the following 5 features:(1)From October 2010 to October 2020,the total joint connectedness of the financial system risks in China showed a relatively high value and fluctuated continuously.(2)In normal times,high-frequency components account for a larger proportion of the total joint connected‐ness,followed by the medium-term components,and finally the long-term components.(3)During the crises,the proportion of the high-frequency components declines rapidly,while that of the medium-and long-term components rises rapidly,which sometimes exceeds the former.(4)It is found that the Covid-19 exerted less influence on investors'mid-to long-term belief changes,and the influence lasts for shorter while analyzing in the perspective of frequency,though the total joint connectedness of the critical event are similar.(5)It is found that large securities companies and joint-stock commercial banks mainly act as risk communicators and occupy a dominant position in financial network risk contagion.However,the four major state-owned commercial banks mainly act as risk receivers,and can play as a stabilizer in the financial system as they have the ability to resist risks.In addition,small securities companies and other financial institutions also act as risk receivers.