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Price volatility in ethanol markets

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  • Serra, Teresa
  • Zilberman, David

Abstract

Our paper looks at how price volatility in the Brazilian ethanol industry changes over time and across markets. Demand and supply forces in the energy and food markets are likely to ensure that crude oil, ethanol and feedstock prices co-move in the long-run. Hence, when assessing price volatility changes and spillovers in the ethanol industry, one should also pay attention to the notion of cointegration. Until recently, the methods proposed to estimate cointegration relationships, have not explicitly considered time varying volatility in the data. Seo (2007) suggests an estimator of the cointegration vector that explicitly models conditional heteroskedasticity. More specifically, he proposes a maximum likelihood estimator that estimates the error correction model and the multivariate GARCH process jointly. We follow this proposal.

Suggested Citation

  • Serra, Teresa & Zilberman, David, 2009. "Price volatility in ethanol markets," 2009 Conference, August 16-22, 2009, Beijing, China 49940, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae09:49940
    DOI: 10.22004/ag.econ.49940
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    References listed on IDEAS

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    1. 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.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    4. 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.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. 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.
    7. repec:cor:louvrp:-1847 is not listed on IDEAS
    8. 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.
    9. Hamelinck, Carlo N & Faaij, Andre P.C., 2006. "Outlook for advanced biofuels," Energy Policy, Elsevier, vol. 34(17), pages 3268-3283, November.
    10. 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.
    11. Oecd, 2006. "Agricultural Market Impacts of Future Growth in the Production of Biofuels," OECD Papers, OECD Publishing, vol. 6(1), pages 1-57.
    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

    Keywords

    Demand and Price Analysis; Research Methods/ Statistical Methods;

    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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