The Effects Of US-China Trade Friction On Chinese Financial Market

This study uses a structural vector autoregression(SVAR)model that is identified via event day heteroscedasticity with daily data to investigate the effects of the US-China trade friction on various Chinese financial variables.Specifically,we determine a set of days on which trade war-related news significantly alters investors’ views about the likely severity and extent of trade tensions and assume that on these days,the related news generates especially high variance,but there is otherwise nothing unusual.Comparing the variance-covariance matrices of VAR residuals on these and other days enables the identification of the effects of this trade war-related news.To investigate the effects of trade war risk on Chinese financial markets,we identify the US-China trade friction risk shock in a structural VAR model and proceed with standard structural analyses such as the impulse responses functions(IRF),forecast error variance decomposition(FEVD),and historical decomposition(HD).The identified trade war risk shock behaves more like an uncertainty shock,which causes declines in equity prices and Treasury yields,rises in soybean future price and stock market volatility,a widening of AAA-BBB corporate yields spread,and a depreciation in the RMB against dollar.More importantly,we find that concerns about risks from the trade war may be overblown and the impacts of USChina trade friction on Chinese financial market is controllable.The innovation of this paper lies in that when studying the impact of Sino-US trade conflicts,it does not rely on abnormal returns of stock market based on statistical models or economic models,but extracts risk shocks from SVAR model to measure the impact of Sino-US trade conflicts on financial markets.