Computational analysis of the real-time impact of uncertainty shocks on price volatility in the container shipping market based on high-frequency data

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 (MVMQ-CAViaR) 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 GARCH-MIDAS 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.