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Abstract
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.