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Price volatility in ethanol markets

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  • Teresa Serra
  • David Zilberman
  • José Gil

Abstract

This research evaluates price volatility transmission in the Brazilian ethanol industry over time and across markets by using a new methodological approach proposed by Seo. The main advantage of Seo's method is that it allows for joint estimation of the co-integration relationship between the price series investigated and the multivariate generalised autoregressive conditional heteroscedasticity process. It thus allows the responses of both food price levels and volatility to unanticipated shocks to be considered together. Results suggest a strong link between food and energy markets, both in terms of price levels and volatility. Oxford University Press and Foundation for the European Review of Agricultural Economics 2010; all rights reserved. For permissions, please email journals.permissions@oup.com, Oxford University Press.

Suggested Citation

  • Teresa Serra & David Zilberman & José Gil, 2011. "Price volatility in ethanol markets," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 38(2), pages 259-280, June.
  • Handle: RePEc:oup:erevae:v:38:y:2011:i:2:p:259-280
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    References listed on IDEAS

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    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. repec:cor:louvrp:-1847 is not listed on IDEAS
    4. Hamelinck, Carlo N & Faaij, Andre P.C., 2006. "Outlook for advanced biofuels," Energy Policy, Elsevier, vol. 34(17), pages 3268-3283, November.
    5. Oecd, 2006. "Agricultural Market Impacts of Future Growth in the Production of Biofuels," OECD Papers, OECD Publishing, vol. 6(1), pages 1-57.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    8. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    9. Myers, Robert J., 1994. "Time Series Econometrics and Commodity Price Analysis: A Review," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 62(02), pages 1-15, August.
    10. Seo, Byeongseon, 2007. "Asymptotic distribution of the cointegrating vector estimator in error correction models with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 137(1), pages 68-111, March.
    11. Kelvin Balcombe & Alastair Bailey & Jonathan Brooks, 2007. "Threshold Effects in Price Transmission: The Case of Brazilian Wheat, Maize, and Soya Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 308-323.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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