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Asymmetry, Risk, and Correlation Dynamics in the U.S. Fiber Market

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  • Fadiga, Mohamadou L.
  • Misra, Sukant K.

Abstract

This study looked at the dynamics of conditional correlations and hedging strategies in the US main cotton producing regions. A two-step procedure was utilized to model, estimate, and analyze volatility, conditional correlations, and the optimal hedge ratios using spot prices in the Delta, Southeast, Southern Plains, and the Southwest regions and the New York commodity exchanges December futures contracts. The results indicate that volatilities in most of the regions are asymmetric and persistent. The derived conditional correlations and the optimal hedging ratios are dynamic although they do not have unit root. Moreover, the changes in agricultural policies altered the dynamics of correlations and producers' hedging strategies in the Delta, Southeast, and Southern Plains regions.

Suggested Citation

  • Fadiga, Mohamadou L. & Misra, Sukant K., 2005. "Asymmetry, Risk, and Correlation Dynamics in the U.S. Fiber Market," 2005 Annual meeting, July 24-27, Providence, RI 19459, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea05:19459
    DOI: 10.22004/ag.econ.19459
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    References listed on IDEAS

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    1. Bollerslev, Tim & Engle, Robert F, 1993. "Common Persistence in Conditional Variances," Econometrica, Econometric Society, vol. 61(1), pages 167-186, January.
    2. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," The Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030.
    5. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    6. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    7. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    8. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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