This paper constructs a cross-market risk spillover effect model and volatility measurement framework based on high-frequency data to study the real-time impact of uncertainty shocks on price volatility in the container transportation market. Through the multivariate multi-quartile conditional autoregressive value-at-risk (MVMQCAViaR) model, we quantify the bi-directional extreme risk spillover effect between shipping and financial markets, and reveal the dynamic transmission direction and absorption capacity of tail risk. Combined with the GARCHMIDAS model, the interaction between low-frequency economic policy uncertainty and high-frequency market volatility is separated, and it is found that the macro factors have a significant impact on the long memory of shipping price volatility. The DCC-MIDAS-CoVaR model is further utilized to analyze the time-varying characteristics of crossmarket linkages under uncertainty shocks, and the results show that there is heterogeneity in the intensity of policy shocks on different market linkages. For the high-frequency volatility measure, the jump component is decomposed by realized volatility with power-of-quadratic variance, which shows that the contribution of jumps to price dispersion accounts for more than 30% of extreme volatility. The empirical part focuses on the containerized freight index, combining 168 monthly data to construct a VAR model with impulse response analysis. The results show that the shocks of WTI and Brent crude oil prices to the freight index are asymmetric. a 1% shock in Brent oil price leads to a peak response of 0.9% in the 2nd period, which is significantly higher than that of WTI’s 0.6%, and the response period extends to 5 periods. There is a 1-2 period lag in the transmission path of the manufacturing PMI index to freight rates, and the demand-side shock decays 40% faster than the supply-side. Through AR root test and dummy variable adjustment, the model effectively captures the impact of structural breakpoints in the freight index.
