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Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

Author

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  • Chia-Lin Chang

    () (Department of Applied Economics, Department of Finance, National Chung Hsing University Taichung, Taiwan)

  • Michael McAleer

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.)

  • Roengchai Tansuchat

    (Faculty of Economics Maejo University Chiang Mai, Thailand)

Abstract

This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.

Suggested Citation

  • Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2012. "Modelling Long Memory Volatility in Agricultural Commodity Futures Returns," Documentos de Trabajo del ICAE 2012-10, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised May 2012.
  • Handle: RePEc:ucm:doicae:1210
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    References listed on IDEAS

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    Cited by:

    1. Aloui, Chaker & Hammoudeh, Shawkat & Hamida, Hela ben, 2015. "Global factors driving structural changes in the co-movement between sharia stocks and sukuk in the Gulf Cooperation Council countries," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 311-329.
    2. Demiralay, Sercan & Ulusoy, Veysel, 2014. "Non-linear volatility dynamics and risk management of precious metals," The North American Journal of Economics and Finance, Elsevier, vol. 30(C), pages 183-202.
    3. Tarek Chebbi & Abdelkader Derbali, 2015. "The dynamic correlation between energy commodities and Islamic stock market: analysis and forecasting," International Journal of Trade and Global Markets, Inderscience Enterprises Ltd, pages 112-126.
    4. David C Broadstock & Rui Wang & Dayong Zhang, 2014. "The direct and indirect effects of oil shocks on energy related stocks," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 146, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    5. Algieri, Bernardina, 2014. "The influence of biofuels, economic and financial factors on daily returns of commodity futures prices," Energy Policy, Elsevier, vol. 69(C), pages 227-247.
    6. Moawia Alghalith & Xu Guo & Wing-Keung Wong & Lixing Zhu, 2016. "A General Optimal Investment Model In The Presence Of Background Risk," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., pages 1-8.
    7. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    8. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
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    11. Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2013. "Long memory and asymmetry in the volatility of commodity markets and Basel Accord: choosing between models," Working Papers 2013-9, Department of Research, Ipag Business School.
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    13. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
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    15. Ederington, Louis H. & Guan, Wei, 2013. "The cross-sectional relation between conditional heteroskedasticity, the implied volatility smile, and the variance risk premium," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3388-3400.
    16. Williams, J., 2013. "Wheat and corn price skewness and volatility: Risk management implications for farmers and end users," Australasian Agribusiness Review, University of Melbourne, Melbourne School of Land and Environment, vol. 21.

    More about this item

    Keywords

    Long memory; agricultural commodity futures; fractional integration; asymmetric; conditional volatility.;

    JEL classification:

    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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