International Journal of Econometrics and Financial Management
ISSN (Print): 2374-2011 ISSN (Online): 2374-2038 Website: Editor-in-chief: Tarek Sadraoui
Open Access
Journal Browser
International Journal of Econometrics and Financial Management. 2015, 3(2), 64-75
DOI: 10.12691/ijefm-3-2-3
Open AccessArticle

Dependence between Non-Energy Commodity Sectors Using Time-Varying Extreme Value Copula Methods

Zayneb Attaf1, , Ahmed Ghorbel1 and Younes Boujelbène1

1Faculty of Economics and Management sciences, University of Sfax, Tunisia

Pub. Date: January 21, 2015

Cite this paper:
Zayneb Attaf, Ahmed Ghorbel and Younes Boujelbène. Dependence between Non-Energy Commodity Sectors Using Time-Varying Extreme Value Copula Methods. International Journal of Econometrics and Financial Management. 2015; 3(2):64-75. doi: 10.12691/ijefm-3-2-3


In this work, our objective is to study the intensity of dependence between six non-energy commodity sectors in a bivariate context. Our methodology is to chose, in a first step, the appropriate copula flowing Akaike criteria. In a second step, we aim to calculate the dependence coefficients (Kendall’s tau, Spearman’s rho and tail dependence) using filtered data by the AR(1)-GARCH(1.1) model to study the dependence between the extreme events. Empirical results show that dependence between non-energy commodity markets increases during volatile periods but they offer many opportunities to investors to diversify their portfolio and reduce their degree of risk aversion in bearish market periods.

non-energy commodity dependence structure copula diversification time-varying correlations

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


[1]  Aloui,R., Ben Aïssa, M.S., & Nguyen, D.K. (2014). Market risk in agricultural commodity markets: a wavelet-based copula modeling approach. Working Paper, 412, Department of Research, Ipag Business School.
[2]  Arezki,R., Dumitrescu,E., Freytag,A & Quintynd,M. (2014). Commodity prices and exchange rate volatility: Lessons from South Africa's capital account liberalization. Journal of Emerging Markets Review, 19, 96-105.
[3]  Baffes John. (2007). Oil spills on others commodities. Journal of Resources Policy, 32, 126-134.
[4]  Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327.
[5]  Ciner, C. (2001). On the long run relationship between gold and silver prices: A note. Global Finance Journal, 12, 299-303.
[6]  Chkili, W,. Hammoudeh, S. & Nguyen, D. K. (2014). Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Journal of Energy Economics, 41, 1-18.
[7]  Chuanguo Zhang & Xiaoqing Chen. (2013). The impact of global oil price shocks on China’s bulk commodity markets and fundamental industries. Journal of Energy Policy. In press.
[8]  Choi, K and Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Journal of Energy Policy, 38, 4388-4399.
[9]  Chong, J. and Miffre, J. (2010). Conditional correlation and Volatility in Commodity Futures and Traditional Asset Markets. Journal of Alternative Investments, 61-75.
[10]  Delatte Anne-Laure & Lopez Claude. (2013). Commodity and equity markets: Some stylized facts from a copula approach. Journal of Banking & Finance, 37, 5346-5356.
[11]  Erb, C.B., Harvey, C.R. (2006). The strategic and tactical value of commodity futures. Financial Analysts Journal, 62, 69-97.
[12]  Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987-1007.
[13]  Forbes, K., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. Journal of Finance, 57, 2223-2261.
[14]  Greer, R. (2000). The nature of commodity index returns. Journal of Alternative Investments, 45-53.
[15]  Gorton, G.B., Rouwenhorst, G.K. (2006). Facts and fantasies about commodity futures. Financial Analysts Journal 62, 47-68.
[16]  Genest, C., Quessy, J,F., & Rémillard, B. (2006). Goodness-of-fit procedures for copula models based on the probability integral transformation. Scandinavian Journal of Statistics, 33, 2, 337-366.
[17]  Hammoudeh, S., Malik, F., & McAleer, M. (2011). Risk management of precious metals. The Quarterly Review of Economics and Finance, 51, 435-441.
[18]  Hammoudeh, S., & Yuan, Y. (2008). Metal volatility in presence of oil and interest rate shocks. Journal of Energy Economics, 30, 2, 606-620.
[19]  Juan C. Reboredo. (2013). Is gold a hedge or safe haven against oil price movements?. Journal of Resources Policy, 38, 130-137.
[20]  Kantaporn, C,. Aree, W,. Songsak, S. & Chukiat, C. (2012). Application of Extreme Value Copulas to Palm oil prices analysis. Journal of Business Management Dynamics, 2, 1, 25-31.
[21]  Li Liu. (2014). Cross-correlations between crude oil and agricultural commodity markets. Journal of Physica A, 395, 293-302.
[22]  Leybourne, S.J., Lloyd, T.A., and Reed, G.V. (1994). The excess Co-movement of Commodity Prices Revisited. World Development, 22, 11, 1747-1758.
[23]  Mensi,W., Hammoudeh,S., Nguyen, D.K &Yoon,S.M. (2014). Dynamic spillovers among major energy and cereal commodity prices. Journal of Energy Economics, 43, 225-243.
[24]  Mensi,W,. Beljid,M,. Boubaker,A., & Managi,S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Journal of Economic Modelling, 32, 15-22.
[25]  Nazlioglu, S,. Erdem, C and Soytas, U. (2013). Volatility spillover between oil and agricultural commodity markets. Journal of Energy Economics, 36, 658-665.
[26]  Nazlioglu S. (2011). World oil and agricultural commodity prices: Evidence from nonlinear causality. Journal of Energy Policy, 39, 2935-2943.
[27]  Patton, A.J. (2007), Estimation of Multivariate Models for Time Series of Possibly Different Lengths, Journal of Applied Econometrics, 21, 2, 147-173.
[28]  Palaskas, T.B., and Varangis, P.N. (1991). Is there Excess Co-movement of primary commodity prices?: A cointegration test. Working paper series, 758, International Economie Department, The World Bank.
[29]  Pindyck, R.S., and Rotemberg, J.J. (1990). The Excess Co-Movement of Commodity Price. The Economic Journal, 100, 403, 1173-1189.
[30]  Power,G.J., Vedenov.D.V., Anderson,D.P., & Klose, S. (2013). Market volatility and the dynamic hedging of multi-commodity price risk. Applied Economics, 45, 3891-3903.
[31]  Qiang Ji and Ying Fan. (2012). How does oil price volatility affect non-energy commodity markets?. Journal of Applied Energy, 89, 273-280.
[32]  Saban Nazlioglu & Ugur Soytas. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Journal of Energy Economics, 34, 1098-1104.
[33]  Sadorsky P. (2014). Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Journal of Energy Economics, 43, 72-81.
[34]  Śmiech, S., & Papież, M. (2012). A dynamic analysis of causality between prices on the metals market. In: Reiff, M. (Ed.), Proceedings of the International Conference Quantitative
[35]  Methods In Economics (Multiple Criteria Decision Making XVI), Bratislava: Slovakia, 221-225.
[36]  Reboredo J.C. (2012). Do food and oil prices co-move?. Journal of Energy Policy,49, 456-467.
[37]  Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges, Vol. 8, pp. 229-231, Publications de l’Institut de Statistique de L’Université de Paris.
[38]  Viviana Fernandez. (2014). Linear and non-linear causality between price indices and commodity prices. Journal of Resources Policy, 41, 40-51.
[39]  Wang,Y., Wu, C and Yang, L. (2014). Oil price shocks and agricultural commodity prices. J Energy Economics, 44, 22-35.