Global food and energy markets: volatility transmission and impulse response effects

Onour, Ibrahim and Sergi, Bruno (2011): Global food and energy markets: volatility transmission and impulse response effects.

Abstract

This paper investigates volatility spillover across crude oil market and wheat and corn markets. The corn commodity is taken here to assess the impact of change in demand for biofuel on wheat market. Results of multivariate GARCH model show evidence of corn price volatility transmission to wheat market . Our results indicate that while shocks (unexpected news) in crude oil market have significant impact on volatility in wheat and corn markets, the effect of crude oil price changes on corn and wheat markets is insignificant. The impulse response analysis indicate shocks in oil markets have permanent effect on food commodity price changes. Also indicated that fertilizers markets influenced by own-shocks and shocks in oil markets.

• Item Type: MPRA Paper
• Language: English
• Keywords: Volatility, global food, impulse response
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q18 - Agricultural Policy ; Food Policy

References

References

Bollerslev, T., Engle, R., and Nelson, D. (2003). Arch Models. In Engle, R. and McFadden, D. (eds.), Handbook of Econometrics, (pp. 2961-3030). Amsterdam, The Netherlands: Elsevier.

Bollerslev,T., Engle R., and Wooldridge J., (1988) “A Capital Asset Pricing Model with Time-Varying Covariances” Journal of Political Economy, 96,116-131.

Bollerslev, T. and Mikkelsen, H. (1996). “Modeling and Pricing Long Memory in Stock Market Volatility,” Journal of Econometrics, 37(1): 151-184.

Du, X., Cindy L., and Dermot, J. (May 2009). Speculation and Volatility Spillover in the Crude Oil and Agricultural Commodity Markets: A Bayesian Analysis. Center for Agricultural and Rural Development, Iowa State University, Working Paper 09-WP 491.

Engle R., and Kroner K.(1995) “Multivariate Simultaneous GARCH” Econometric Theory, 11: 122-150.

Engle, R.F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50(4): 987-1008.

------------- (2002). “New Frontiers for ARCH Models,” Journal of Applied Econometrics, 17(5): 425-446.

Engle, R. and Bollerslev, T. (1986). “Modeling the Persistence of Conditional Variances,” Econometric Reviews, 5(1): 1-50.

FAO (Food and Agricultural Organization) (2008). Rome: Food and Agricultural Organization: Food Outlook Report.

Granger, C. and Ding, Z. (1996). “Varieties of Long Memory Models,” Journal of Econometrics, 37(1): 61-78.

Institute for Agriculture and Trade Policy (IATP) (2008). Commodity Market Speculation: The Risk to Food Security and Agriculture. Minneapolis, Minnesota, Report.

Jeffrey F.,(2008a) “An Explanation for Soaring Commodity Prices” VOX, March 25th . Available at http://www.voxeu.org/index.php?q=node/1002

Jeffrey F.,(2008b) “Monetary Policy and Commodity Prices” VOX, March 29th . Available at http://www.voxeu.org/index.php?q=node/1178

Kroner, K., Kneafsey, D., and Classens S. (1993). Forecasting Volatility in Commodity Markets. The World Bank, Policy Research Working Paper (WPS 1226).

Onour, I. (2010). “Global Food Crisis and Crude Oil Price Changes: Do They Share Common Cyclical Features?,” International Journal of Economic Policy in Emerging Economies, 3(1): 61-70.

Onour, I. and Sergi, B.S. (2011). “Modeling and Forecasting Volatility in Global Food Commodity Prices,” Agricultural Economics, 57(3): 132-139.

Whistler, D., White, K., Wong, D., and Bates, D. (2004). Shazam Software, and Users Reference Manual, Version 10. Vancouver, Canada: Northwest Econometrics, Ltd.