Abstract
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.
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References
OECD-FAO, Agricultural Outlook 2010-2019. OECD/FAO (2010)
Saban, N.: World oil and agricultural commodity prices: Evidence from nonlinear causality. Energy Policy 39(5), 2935–2943 (2011)
Tverberg, G.E.: Oil supply limits and the continuing financial crisis. Energy 37(1), 27–34 (2012)
Hanson, K.A., Sherman, R., Schluter, G.E.: Sectoral Effects of a World Oil Price Shock: Economywide Linkages to the Agricultural Sector. Journal of Agricultural and Resource Economics 18(01), 96–116 (1993)
Baffes, J.: More on the energy/nonenergy price link. Applied Economics Letters 17(16), 1555–1558 (2010)
Tang, K., Xiong, W.: Index Investment and Financialization of Commodities. SSRN eLibrary (2010)
Chen, S.T., Kuo, H.I., Chen, C.C.: Modeling the relationship between the oil price and global food prices. Applied Energy 87(8), 2517–2525 (2010)
Nazlioglu, S., Soytas, U.: Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics 34(4), 1098–1104 (2012)
Chang, T.H., Su, H.M.: The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy 35(7), 2807–2813 (2010)
Yu, T.E., Bessler, D.A., Fuller, S.: Cointegration and causality analysis of world vegetable oil and crude oil prices. Paper Presented at the The American Agricultural Economics Association Annual Meeting (2006)
Gilbert, C.L.: How to Understand High Food Prices. Journal of Agricultural Economics 61(2), 398–425 (2010)
Natanelov, V., Alam, M.J., McKenzie, A.M., et al.: Is there co-movement of agricultural commodities futures prices and crude oil? Energy Policy 39(9), 4971–4984 (2011)
Cifarelli, G., Paladino, G.: Oil price dynamics and speculation: A multivariate financial approach. Energy Economics 32(2), 363–372 (2010)
Chang, C.L., Khamkaew, T., McAleer, M., et al.: Modelling conditional correlations in the volatility of Asian rubber spot and futures returns. Mathematics and Computers in Simulation 81(7), 1482–1490 (2011)
Wu, C.C., Chung, H., Chang, Y.H.: The economic value of co-movement between oil price and exchange rate using copula-based GARCH models. Energy Economics 34(1), 270–282 (2012)
Patton, A.J.: Modelling asymmetric exchange rate dependence. International Economic Review 47(2), 527–556 (2006)
Ling, S.: Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models. Journal of Econometrics 140(2), 849–873 (2007)
Bollerslev, T., Wooldridge, J.M.: Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews 11(2), 143–172 (1992)
Hansen, B.E.: Autoregressive conditional density estimation. International Economic Review 35(3), 705–730 (1994)
Cherubini, U., Luciano, E., Vecchiato, W.: Copula Methods in Finance. John Willey & Sons, Chichester (2004)
Brechmann, E.C.: Truncated and simplied regular vines and their applications. Technische Universitaet Muenchen (2010)
Genest, C., Rémillard, B., Beaudoin, D.: Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics 44(2), 199–213 (2009)
Diebold, F.X., Gunther, T.A., Tay, A.S.: Evaluating Density Forecasts with Applications to Financial Risk Management. International Economic Review 39(4), 863–883 (1998)
Reboredo, J.C.: Do food and oil prices co-move? Energy Policy 49(0), 456–467 (2012)
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Boonyanuphong, P., Sriboonchitta, S., Chaiboonsri, C. (2013). Modeling Dependency of Crude oil Price and Agricultural Commodity Prices: A Pairwise Copulas Approach. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Uncertainty Analysis in Econometrics with Applications. Advances in Intelligent Systems and Computing, vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35443-4_18
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DOI: https://doi.org/10.1007/978-3-642-35443-4_18
Publisher Name: Springer, Berlin, Heidelberg
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