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

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Author Info

  • 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.

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File URL: http://www.econ.qmul.ac.uk/papers/doc/wp594.pdf
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Bibliographic Info

Paper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 594.

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Date of creation: Apr 2007
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Handle: RePEc:qmw:qmwecw:wp594

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Keywords: Commodity returns; Futures markets; Long memory; FIGARCH;

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References

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  1. Baillie, Richard T & Bollerslev, Tim, 1989. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 297-305, July.
  2. Hyun J. Jin & Darren L. Frechette, 2004. "Fractional Integration in Agricultural Futures Price Volatilities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 432-443.
  3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  4. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
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Cited by:
  1. Serpil TURKYILMAZ & Mesut BALIBEY, 2014. "Long Memory Behavior in the Returns of Pakistan Stock Market: ARFIMA-FIGARCH Models," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 400-410.

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