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The influence of behavior factors in setting the agricultural futures market prices

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  • Serrao, Amilcar

Abstract

The great challenge for this research work is to show that the biases of investor behavior are predictable and may affect the coffee futures market prices. This research work uses auto-regressive conditional heteroskedasticity (ARCH) models to analyze results that cause deviations in the coffee futures market prices. The negative asymmetry coefficient of EGARCH model and the positive asymmetry coefficient of TGARCH model show the presence of the leverage effect where negative shocks have a greater impact in the volatility of returns in coffee than positive shocks. The presence of the leverage effect corroborates the Prospect Theory. Model results also show that the reactions of investors to negative information were statistically significant in the coffee futures market and suggest that Behavioral Finance might contribute to the understanding of the formation of coffee futures market prices.

Suggested Citation

  • Serrao, Amilcar, 2014. "The influence of behavior factors in setting the agricultural futures market prices," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170326, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea14:170326
    DOI: 10.22004/ag.econ.170326
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Matthew Rabin, 1998. "Psychology and Economics," Journal of Economic Literature, American Economic Association, vol. 36(1), pages 11-46, March.
    3. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    4. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    5. 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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