In this paper, we introduce the copula approach to the empirical research of asymmetric price transmission. The proposed methodology serves as an appropriate improvement for investigating price co-movement and market integration as it allows for flexible dependence/ structures among price adjustments/reactions along supply chain markets. In addition, we address the potential bias and inconsistency issue that results from ignorance of the volatility trait of price or price changes. In the empirical application, we exploit a state-dependent copula method, with generalized autoregressive conditional heteroskedasticity (GARCH) as marginals, to construct bivariate dynamic copula-GARCH models in the U.S. hog supply chain. The method can simultaneously capture both volatility in univariate price changes and dynamic relationships among price movements.