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Modelling Long Memory Volatility In Agricultural Commodity Futures Returns

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  • CHIA-LIN CHANG

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

  • MICHAEL McALEER

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

  • 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 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

Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal Annals of Financial Economics.

Volume (Year): 07 (2012)
Issue (Month): 02 ()
Pages: 1250010-1-1250010-27

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Handle: RePEc:wsi:afexxx:v:07:y:2012:i:02:p:1250010-1-1250010-27

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Related research

Keywords: Long memory; agricultural commodity futures; fractional integration; asymmetric; conditional volatility; Q14; Q11; C22; C51;

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Cited by:
  1. repec:ipg:wpaper:9 is not listed on IDEAS
  2. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.
  3. 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 2014-325, Department of Research, Ipag Business School.
  4. 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.
  5. 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.
  6. 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.
  7. 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.

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