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Modelling Long Memory Volatility in Agricultural Commodity Futures Return

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Author Info

  • Michael McAleer

    (Erasmus University Rotterdam,Tinbergen Institute,Kyoto University,Complutense University of Madrid)

  • Chia-Lin Chang

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

  • 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.

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Bibliographic Info

Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 817.

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Date of creation: May 2012
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Handle: RePEc:kyo:wpaper:817

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

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  1. Shiqing Ling & Michael McAleer, 2001. "Necessary and Sufficient Moment Conditions for the GARCH(r,s) and Asymmetric Power GARCH(r,s) Models," ISER Discussion Paper 0534, Institute of Social and Economic Research, Osaka University.
  2. Peter S. Sephton, 2009. "Fractional integration in agricultural futures price volatilities revisited," Agricultural Economics, International Association of Agricultural Economists, vol. 40(1), pages 103-111, 01.
  3. Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006,13, Christian-Albrechts-University of Kiel, Department of Economics.
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Cited by:
  1. Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Working Papers 2014-389, Department of Research, Ipag Business School.
  2. 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.
  3. 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 2013-009, Department of Research, Ipag Business School.
  4. repec:ipg:wpaper:9 is not listed on IDEAS
  5. David C Broadstock & Rui Wang & Dayong Zhang, 2014. "The direct and indirect e ects 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.
  6. 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.
  7. 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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