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Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices

Author

Listed:
  • Richard T. Baillie

    (Michigan State University and Queen Mary, University of London)

  • Young-Wook Han

    (Hallym University, Chunchon)

  • Robert J. Myers

    (Michigan State University)

  • Jeongseok Song

    (Chung-Ang University, Seoul)

Abstract

Daily futures returns on six important commodities are found to be well described as FIGARCH fractionally integrated volatility processes, with small departures from the martingale in mean property. The paper also analyzes several years of high frequency intra day commodity futures returns and finds very similar long memory in volatility features at this higher frequency level. Semi parametric Local Whittle estimation of the long memory parameter supports the conclusions. Estimating the long memory parameter across many different data sampling frequencies provides consistent estimates of the long memory parameter, suggesting that the series are self-similar. The results have important implications for future empirical work using commodity price and returns data.

Suggested Citation

  • Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:594
    as

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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2007/items/wp594.pdf
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    References listed on IDEAS

    as
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    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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