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Forecasting long memory processes subject to structural breaks

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Author Info

  • WANG, Shin-Huei

    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium; National TsingHwa University, Taiwan)

  • BAUWENS, Luc

    ()
    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • HSIAO, Cheng

    (Department of Economics, University of Southern California, USA; City University of Hong Kong; WISE, Xiamen University, China)

Abstract

We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC or Mallows' Cp) to choose the order of the approximate AR model. Our method avoids the issue of estimation inaccuracy of the long memory parameter and the issue of spurious breaks in finite sample. Insights from our theoretical analysis are confirmed by Monte Carlo experiments, through which we also find that our method provides a substantial improvement over existing prediction methods. An empirical application to the realized volatility of three exchange rates illustrates the usefulness of our forecasting procedure. The empirical success of the HAR-RV model is explained, from an econometric perspective, by our theoretical and simulation results.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2012048.

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Date of creation: 10 Dec 2012
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Handle: RePEc:cor:louvco:2012048

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Keywords: forecasting; long memory process; structural break;

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References

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  1. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, April.
  2. Richard T. Baillie & Claudio Morana, 2007. "Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach," Working Papers 593, Queen Mary, University of London, School of Economics and Finance.
  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.
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  5. Kyongwook Choi & Wei-Choun Yu & Eric Zivot, 2008. "Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility," Working Papers UWEC-2008-20-FC, University of Washington, Department of Economics.
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  11. Pesaran, M.H. & Timmermann, A., 2003. "Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks," Cambridge Working Papers in Economics 0331, Faculty of Economics, University of Cambridge.
  12. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
  13. CESI, Berardino & PAOLINI, dimitri, 2012. "Peer group and distance: when widening university participation is better," CORE Discussion Papers 2012042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  14. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
  15. Fleurbaey,Marc & Maniquet,François, 2011. "A Theory of Fairness and Social Welfare," Cambridge Books, Cambridge University Press, number 9780521887427, April.
  16. Hosking, Jonathan R. M., 1996. "Asymptotic distributions of the sample mean, autocovariances, and autocorrelations of long-memory time series," Journal of Econometrics, Elsevier, vol. 73(1), pages 261-284, July.
  17. 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.
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