摘要
金融系统处于高压力状态会影响其正常的资源配置功能,极端情况下可能引发金融危机。目前金融压力指数已经成为评估金融市场稳定性及货币政策效率的重要工具,但国内研究大多利用月度数据进行压力指数构建,时效性不足,无法为监管者或市场参与者提供及时有效的信息。本文从货币、债券、股票、外汇四个子市场选取11个日度市场指标分别构建子市场压力指数。在集成整体金融压力指数时,依据子市场压力对经济增长的影响确定子市场的"基础权重",并进一步借鉴资产投资组合理论考虑子市场间的时变相关结构对整体压力指数的即时影响。通过中国金融市场典型事件验证了本文构建的金融压力指数的有效性,为进一步研究中国金融市场提供了理论和数据基础。
High tension in the financial system will affect its resource allocation function and further lead to economic downturns.An extreme state of financial stress is the financial crisis.Financial stress index has become an important instrument and reference index to evaluate the efficiency of monetary policy and instability of the financial system.However,in the construction process of index,selection of indicators,data frequency and aggregation methods,there does not exist a unified standard.Financial stress index in existing studies is generally constructed based on monthly data,which leads to some limits in timeliness.As a result,it cannot provide enough immediate and effective information to assist the decision-making.Eleven daily indicators of money,bond,equity and exchange markets are chosen to construct financial stress index in China after investigating the existing literature.When aggregating these indicators,basic weights of four sub-markets are determined according to their influence on economy.Further,similar to portfolio theory,a time-varying correlation between different sub-markets is introduced to ensure time-varying characteristic of weight.Validity of the constructed index is verified through an analysis of stress events in the financial system of China,so this finding lays a foundation for further studies.
作者
姚晓阳
孙晓蕾
李建平
Yao Xiaoyang;Sun Xiaolei;Li Jianping(College of Economics&Management,China Jiliang University,Hangzhou 310018;Institutes of Science and Development,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)
出处
《管理评论》
CSSCI
北大核心
2019年第4期34-41,共8页
Management Review
基金
国家自然科学基金项目(71425002
71373009
71133005)
关键词
金融压力
相关性
时变结构
投资组合
financial stress
high-frequency data
time-varying structure
portfolio theory