The main objective of this thesis is to examine the dependency structure among different agricultural and energy commodity markets in the United States. For achieving this goal, the paper makes use of the Copula-GARCH(1,1) model to study the financial return volatility and the co-movement between pair of commodities including corn, soybean and gasoline over the pre-COVID 19 pandemic period (from 01-01-2018 to 01-01-2020) and the ongoing COVID 19 pandemic period (from 01-01-2020 to 01-04-2021). First, the study has shown that the time-dependent volatilities of commodity returns display volatility clustering effect in the two periods and the volatility of volatility of commodity markets is higher during the pandemic period. Second, it is observed that the correlations among different commodities have increased significantly in the ongoing pandemic period and we also find that the strongest co-movement is between returns of corn and soybean over the two periods. Finally, the results suggest that the (extreme) co-movements between agricultural commodities (corn and soybean) are governed by symmetry; that is they tend to boom and crash together during extreme shocks or events. On the other hand, the (extreme) co-movements between an agricultural commodity (corn or soybean) and the energy commodity (gas) appear to co-move asymmetrically and they tend to experience the market crash together but not the market boom.