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Modelling Return and Volatility of Oil Price using Dual Long Memory Models

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  • Heni BOUBAKER
  • Nadia SGHAIER

Abstract

This paper investigates the dynamic properties of both return and volatility of the oil price. The analysis is carried out using a set of double long memory specifications incorporating several features such as long range dependence, asymmetry in condit

Suggested Citation

  • Heni BOUBAKER & Nadia SGHAIER, 2014. "Modelling Return and Volatility of Oil Price using Dual Long Memory Models," Working Papers 2014-283, Department of Research, Ipag Business School.
  • Handle: RePEc:ipg:wpaper:2014-283
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    File URL: https://faculty-research.ipag.edu/wp-content/uploads/recherche/WP/IPAG_WP_2014_283.pdf
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    References listed on IDEAS

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    1. 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.
    2. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    3. Karanasos, M. & Sekioua, S.H. & Zeng, N., 2006. "On the order of integration of monthly US ex-ante and ex-post real interest rates: New evidence from over a century of data," Economics Letters, Elsevier, vol. 90(2), pages 163-169, February.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    6. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    7. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    8. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    9. Richard T. Baillie & Young Wook Han & Tae-Go Kwon, 2002. "Further Long Memory Properties of Inflationary Shocks," Southern Economic Journal, John Wiley & Sons, vol. 68(3), pages 496-510, January.
    10. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    11. Y. K. Tse, 1998. "The conditional heteroscedasticity of the yen-dollar exchange rate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 49-55.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Keywords

    Oil price; return; volatility; dual long memory.;
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