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Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

Author

Listed:
  • Roengchai Tansuchat

    (Faculty of Economics, Maejo University)

  • Chia-Lin Chang

    (Department of Applied Economics, National Chung Hsing University)

  • Michael McAleer

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)

Abstract

This paper estimates the 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), FIEGACH 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

  • Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2009. "Modelling Long Memory Volatility in Agricultural Commodity Futures Returns," CIRJE F-Series CIRJE-F-680, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2009cf680
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    References listed on IDEAS

    as
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    More about this item

    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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