摘要
随着人民币国际化进程的逐步推进,SDR货币篮子中人民币的国际化定位引人瞩目。本文基于非线性MSBIARCH模型,实时甄别人民币市场与美元市场、英镑市场、日元市场、欧元市场之间的波动传染关系,以及波动传染作用下汇率市场的波动聚类态势,进而识别SDR货币篮子中人民币的国际化定位,旨在为及时防范并规避人民币市场的波动风险提供参考。研究发现,汇率市场经由“经济基本面”“市场情绪”以及“市场预期”对外发挥波动传染作用,人民币市场与美元市场之间存在双向波动传染关系,与英镑市场、欧元市场以及日元市场之间存在单向波动传染关系。不同汇率市场之间的波动传染关系表现出时间区制转移特征,汇率市场的波动聚类态势也呈现时变特征。汇率市场发挥波动传染作用的时间与汇率市场呈现波动聚类态势的时间相匹配,均集中在极端经济事件期、不规则事件期以及政策颁布事件期。国际汇率市场的波动传染作用导致了人民币市场的波动聚类态势,而人民币市场的波动传染作用仅强化了国际汇率市场的波动聚类态势,SDR货币篮子中人民币的国际化程度有待进一步提高。
The RMB was included in the Special Drawing Rights(SDR) currency basket on October 1, 2016, following the Dollar, Pound, Yen, and Euro. However, the U.S. has increased protectionist trade policies and tariff barriers since 2018. On March 22, the U.S. announced a large-scale tariff on Chinese imports. The exchange market’s CNY faces a turbulent environment, and China’s economy is experiencing only moderate growth. As a result, the RMB is showing a weak trend and increased pressure. Fluctuations in the CNY have become the market’s primary concern. Therefore, the volatility spillover between the CNY and other international markets, volatility clustering, and the degree of RMB must be explored to prevent and avoid volatile risk with the CNY.The volatility spillover effect stipulates that an economy’s exchange market is more likely to spill over into other economies’ exchange markets when its currency has a higher degree. In addition, other economies show a volatility clustering trend. The comparison between the RMB market and other major currency markets shows the degree of RMB in global economic patterns. Therefore, we use the SDR currency basket’s exchange market to identify the relationship between volatility spillover and volatility clustering in real time and identify the international degree of RMB.A nonlinear model with structural changes is best suited to describe the characteristics of the exchange market’s volatility, given the various time and political characteristics of major global currencies’ co-movement. The Markov switching model can depict the structural change in time series data and internalize the structural change into regime change. The model can describe the regime state in different stages and the nonlinear transition of a regime state in each stage. It is suitable for measuring the stage-switching of volatility spillover between exchange rate markets and the dynamic volatility clustering paths. Therefore, we construct a nonlinear model of Markov switching bivariate ARCH(MSBIA
作者
隋建利
刘碧莹
SUI Jianli;LIU Biying(Quantitative Research Center of Economics,Jilin University)
出处
《金融研究》
CSSCI
北大核心
2020年第11期1-20,共20页
Journal of Financial Research
基金
国家自然科学基金面上项目(71573104)
吉林大学青年学术领袖培育计划项目(2019FRLX10)
吉林大学廉政建设专项研究项目(2020LZY014)
吉林大学研究生创新基金资助项目(101832018C159)的资助。
