This paper aims to study the structure of interdependencies between the energy, biofuel and agricultural commodity markets. The work concentrates on the dependence between ethanol and agricultural futures returns conditional to crude oil returns, and interdependence among agricultural commodities conditional to crude oil and ethanol futures returns. The C-vine copula based ARMA-GARCH model was used to explain the dependence structure of crude oil and the four related variables, and applied to investigate the risk of energy-agricultural commodity futures portfolio.We generally found symmetry in the tail dependence between the energy, biofuel, and agricultural commodities, and also found a greater significant variability in dependence, specifically, the dependence between the ethanol and agricultural commodity futures returns conditional to crude oil as well as interdependence between corn and soybean conditional to crude oil and ethanol return. This indicates that there is a rise in ethanol productions and that higher crude oil prices have caused a price increase in agricultural commodities such as corn and soybean. Moreover, the higher dynamic dependence and symmetric tail dependences indicate that opportunities for portfolio diversification are reduced, particularly during a downturn in the markets. Finally, our result suggests that the time-varying copula model captures the portfolio risk better than the static copula models.

This paper aims at studying the volatility and dependence structureamong the main agricultural commodity markets. It also investigates the impactof the trading activity of agricultural commodities and the ethanol listing on thevolatility transmission for the corn, soybean, and wheat markets. The C- and D-vine copula based GARCH model was used to explain the interdependence ofcorn, soybean, and wheat prices. We discovered that the listing of ethanol andthe trading activity had an impact on the price volatility of corn, soybean, andwheat. The results support the argument that the roles of nancialization and ofthe biofuel increase volatility in the agricultural commodity markets. Moreover,the dependencies between the corn and the wheat returns, and between the cornand the soybean returns have signicant variability over time and have highervariations of dependence with symmetrical tail dependences. The higher dynamicdependence and symmetric tail dependence indicate that opportunities to use therelated agricultural commodities for portfolio diversication are reduced, particu-larly during a downturn in the markets.

This paper aims to estimate the dependency between spot rubber price and futures prices using the copula-extreme value theory based on semi-parametric approaches, which combine copula functions with the conditional extreme value theory to construct the dependence models. The C-EVT model is used to estimate the marginal distributions of the returns of rubber spot price and futures prices that enable the model’s flexibility for the tail behavior. Both static and time-varying copulas are applied to construct the dependence structure between the returns of the rubber spot price and the futures prices. The empirical results showed weak spotfutures dependence between the spot rubber price and the futures prices of Thai markets, implying that we could not accept the efficient market hypothesis. However, we found symmetric tail dependence between the spot rubber price and the futures prices of the Singapore, Tokyo, and Shanghai markets. This means that cash rubber price is do minated by the futures prices of the Singapore, Tokyo, and Shanghai markets. The best-fitting dependence models are the time-varying t-copulas, but the tail dependence for all pairs is relatively low. This result means that the futures prices are weak in explaining the changes in spot prices under extreme events.

This study examines the dependency between the return of crude oil future prices and the agricultural commodity future prices as well as provides flexible models for dependency and the conditional volatility GARCH. Therefore, this paper used copula-based GARCH models, which consists in estimating the marginal distributions of the return of the crude oil price and agricultural commodity prices and then estimates the copula parameters by static and time-varying copula models. The results revealed that the co-movement between crude oil price and agricultural commodity prices are generally strong and there exists symmetric tail dependence between crude oil and agricultural commodity prices in all pairs. However, its tail dependence is relatively weak. The dependence parameters are very volatile over time and deviate from their constant levels. Our findings have important implications for policy makers, producers and traders, which could be used to implement a better policy to optimize and stabilize the markets or their portfolio management in the agricultural commodity markets.