Agric. Econ. - Czech, 2011, 57(3):132-139 | DOI: 10.17221/28/2010-AGRICECON
Modeling and forecasting volatility in global food commodity prices
- 1 Arab Planning Institute, Kuwait, Kuwait
- 2 Faculty of Political Science, University of Messina, Messina, Italy
To capture the volatility in the global food commodity prices, we employed two competing models, the thin tailed the normal distribution, and the fat-tailed Student t-distribution models. Results based on wheat, rice, sugar, beef, coffee, and groundnut prices, during the sample period from October 1984 to September 2009, show the t-distribution model outperforms the normal distribution model, suggesting that the normality assumption of residuals which are often taken for granted for its simplicity may lead to unreliable results of the conditional volatility estimates. The paper also shows that the volatility of food commodity prices characterized with the intermediate and short memory behavior, implying that the volatility of food commodity prices is mean reverting.
Keywords: volatility, forecast, fat-tail distribution, food commodities
Published: March 31, 2011 Show citation
References
- Babula R., Somwaru A. (1992): Dynamic impacts of a shock in crude oil price on agricultural chemical and fertilizer prices. Agribusiness, 8: 243-252.
Go to original source...
- Bai J., Serena Ng (2005): Tests for skewness, kurtosis, and normality for time series data. Journal of Business and Economic Statistics, American Statistical Association, 23: 49-60.
Go to original source...
- Baillie R. (1996): Long memory process and fractional integration in econometrics. Journal of Econometrics, 37: 5-60.
Go to original source...
- Barone-Adesi G., Whaley R. (1987): Efficient analytic approximation of American option values. Journal of Finance, 42: 301-320.
Go to original source...
- Black F., Scholes M. (1993): The pricing of options and corporate liabilities. Journal of Political Economy, 81: 637-659.
Go to original source...
- Bollerslev T., Engle R., Nelson D. (2003): ARCH Models. In: Editors Engle R., McFadden D. (eds.): Handbook of Econometrics, Vol. 4, chapter 39.
- Bollerslev T., Mikkelsen H. (1996): Modeling and pricing long memory in stock market volatility. Journal of Econometrics, 37: 151-184.
Go to original source...
- Brooks C., Persand G., (2003): The effect of asymmetries on stock index returns value-at-risk estimates. Journal of Risk Finance, 4: 29-42.
Go to original source...
- Cunado J., Gil-Alana L., Perez de Gracia F. (2005): A test for rational bubbles in the NASDAQ stock index: a fractionally integrated approach. Journal of Banking & Finance, 29: 2633-2654.
Go to original source...
- Diebold F., Mariano R. (1995): Comparing predictive accuracy. Journal of Business and Economic Statistics, 13: 253-263.
Go to original source...
- Diebold F., Rudebusch G. (1991): On the power of DickeyFuller tests against fractional alternatives. Econometric Letters, 35: 155-160.
Go to original source...
- Ding Z., Granger C. (1996): Modeling volatility persistance of speculative returns: a new approach. Journal of Econometrics, 37: 185-216.
Go to original source...
- Du X., Cindy L., Dermot J. (2009): Speculation and Volatility Spillover in the Crude Oil and Agricultural Commodity Markets: A Bayesian Analysis. Working Paper 09-WP 491, Center for Agricultural and Rural Development, Iowa State University. Available at www.card.iastat. edu
- Engle R. (2002): New frontiers for arch models. Journal of Applied Econometrics, 17: 425-446.
Go to original source...
- Engle R., Bollerslev T. (1986): Modeling the persistence of conditional variances. Econometric Reviews, 5: 1-50.
Go to original source...
- Engle R.F. (1982): Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50: 987-1008.
Go to original source...
- FAO (Food and Agricultural Organization) (2008): Food Outlook Report, June.
- Granger C., Ding Z. (1996): Varieties of long memory models. Journal of Econometrics, 37: 61-78.
Go to original source...
- Hamilton J. (1994): Time Series Analysis. Princeton University Press, Princeton, New Jersey.
- Hansen P., Launde A. (2003): A Forecast Comparison of Volatility Models: Does anything Beat a GARCH(1,1)? Working Paper, Brown University, Department of Economics.
- IATP (Institute for Agriculture and Trade Policy) (2008): Commodity Market Speculation: The Risk to Food Security and Agriculture. Report, Minneapolis, Minnesota, November.
- Kroner K., Kneafsey D., Classens S. (1993): Forecasting Volatility in Commodity Markets. Policy Research Working Paper 1226, The World Bank, Washington.
- McLeod A., Hipel K. (1978): Preservation of the rescaled adjusted range 1: A reassessment of the Hurst phenomenon. Water Resource Research, 14: 491-508.
Go to original source...
- Shephard N. (2005): Stochastic Volatility: Selected Reading. Oxford University Press, New York.
Go to original source...
- Taylor S. (1994): Modelling stochastic volatility. Mathematical Finance, 4: 183-204.
Go to original source...
- Uri N. (1996): Changing crude oil price effects on U.S. agricultural employment. Energy Economics, 18: 185- 202.
Go to original source...
- Vilasuso J. (2002): Forecasting exchange rate volatility. Economic Letters, 76: 59-64.
Go to original source...
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.