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Long memory in commodity futures volatility: A wavelet perspective

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  • John Elder
  • Hyun J. Jin

Abstract

The authors reexamine the volatility of agricultural commodity futures for evidence of fractional integration, providing new empirical results and extending the extant literature in important dimensions. First, they utilize two relatively new estimators based on wavelets, which are generally superior to, for example, the popular estimator by J. Geweke and S. Porter‐Hudak (GPH; 1983) and exact maximum likelihood estimators (MLEs) on the basis of mean squared error (MSE). Second, they provide simulations to contrast their point estimates with those obtained by a fractionally integrated GARCH (generalized autoregressive conditional heteroscedasticity) model. Third, they conduct a wavelet coef.cient decomposition of futures volatility. They .nd that futures volatilities display the self‐similarity property consistent with long memory and that futures volatilities exhibit persistent long memory with .nite unconditional variance. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:411–437, 2007

Suggested Citation

  • John Elder & Hyun J. Jin, 2007. "Long memory in commodity futures volatility: A wavelet perspective," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(5), pages 411-437, May.
    • Handle: RePEc:wly:jfutmk:v:27:y:2007:i:5:p:411-437
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    Cited by:

    1. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    2. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2022. "Forecasting realized volatility of agricultural commodities," International Journal of Forecasting, Elsevier, vol. 38(1), pages 74-96.
    3. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    4. 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, January.
    5. Orlov, Vitaly & Äijö, Janne, 2015. "Benefits of wavelet-based carry trade diversification," Research in International Business and Finance, Elsevier, vol. 34(C), pages 17-32.
    6. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    7. Syed Jawad Hussain Shahzad & Naveed Raza & Aneese Hayat Awan, 2014. "Commodities and Stock Investment," SAGE Open, , vol. 4(3), pages 21582440145, September.
    8. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    9. John Elder & Sriram Villupuram, 2012. "Persistence in the return and volatility of home price indices," Applied Financial Economics, Taylor & Francis Journals, vol. 22(22), pages 1855-1868, November.
    10. Anderson Antonio Denardin & Alice Kozakevicius & Alex A. Schmidt, 2018. "Avaliação Da Medida De Núcleo De Inflação Baseada No Método Wavelet Para O Brasil," Anais do XLIV Encontro Nacional de Economia [Proceedings of the 44th Brazilian Economics Meeting] 34, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    11. Jerry Coakley & Jian Dollery & Neil Kellard, 2011. "Long memory and structural breaks in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(11), pages 1076-1113, November.
    12. Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah & Sjö, Bo, 2016. "On the time scale behavior of equity-commodity links: Implications for portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 30-46.
    13. Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023. "El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 785-801, July.
    14. Jin, Hyun Joung, 2008. "A Long Memory Conditional Variance Model for International Grain Markets," Journal of Rural Development/Nongchon-Gyeongje, Korea Rural Economic Institute, vol. 31(2), pages 1-23, May.
    15. Chen, Feier & Miao, Yuqi & Tian, Kang & Ding, Xiaoxu & Li, Tingyi, 2017. "Multifractal cross-correlations between crude oil and tanker freight rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 344-354.
    16. Williams, J., 2013. "Wheat and corn price skewness and volatility: Risk management implications for farmers and end users," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 21, pages 1-20.

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