Balcombe, Kelvin (2009): The Nature and Determinants of Volatility in Agricultural Prices.
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The volatility of 19 agricultural commodity prices are examined at monthly and annual frequencies. All of the price series are found to exhibit persistent volatility (periods of relatively high and low volatility). There is also strong evidence of transmission of volatilities across prices. Volatility in oil prices is found to be a significant determinant of volatilities in the majority of series and, likewise, exchange rate volatility is found to be a predictor of volatility in over half the series. There is also strong evidence that stock levels and yields are influencing price volatility. Most series exhibit significant evidence of trends in their volatility. However, these are in a downward direction for some series and in an upward direction for other series. Thus, there is no general finding of long term increases in volatility across most agricultural prices
|Item Type:||MPRA Paper|
|Original Title:||The Nature and Determinants of Volatility in Agricultural Prices|
|Keywords:||Volatility, Agricultural Prices|
|Subjects:||N - Economic History > N5 - Agriculture, Natural Resources, Environment, and Extractive Industries
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Kelvin Balcombe|
|Date Deposited:||07. Sep 2010 18:43|
|Last Modified:||13. Feb 2013 20:05|
References J Aizeman and B Pinto (2005) Managing Economic Volatility and Crisis, A practitioners Guide, Cambridge University Press. New York. World Bank (2005).
Balcombe K. and Rapsomanikis G (2008). Bayesian Estimation and Selection of Non‐Linear Vector Error Correction Models: The Case of the Sugar‐Ethanol‐Oil Nexus in Brazil. American Journal of Agricultural Economics, 90 (2) 658-668.
Chib C. and E. Greenberg, (1995). Understanding the Metropolis-Hastings Algorithm. The American Statistician, November, 1995, 49. No 4.: 327-335
Deaton A and Laroque G. (1992) On the behaviour of Commodity Prices. Review of Economic Studies, 59, 1-23.
Engle R.F. (1982). Autoregressive Conditional Heteroscedasticity of the Variance of United Kingdom Inflation. Econometrica. 50,4 987-1006.
Engle R.F (1995) . ARCH, Selected Readings. Advanced Texts in Econometrics. Oxford University Press. FAO (2008), Food Outloook, Global Market Analysis. June, http://www.fao.org/docrep/010/ai466e/ai466e00.HTM
Harvey A.C. (1989). Forecasting structural time series models and the Kalman filter. Cambridge University Press. Cambridge.
Lex Oxley, Donald A. R., Colin J., Stuart Sayer (1994) Surveys in Econometrics. Wiley Blackwell.
Kenneth Train (2003). Discrete Choice Methods with Simulation. Cambridge University Press, 2003
Koop G. (2003) Bayesian Econometrics, Wiley, Sussex, England.