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Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility

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Author Info

  • Kyongwook Choi

    (Department of Economics, The University of Seoul,)

  • Wei-Choun Yu

    (Economics and Finance Department, Winona State University)

  • Eric Zivot

    (Department of Economics, University of Washington)

Abstract

We explore the possibility of structural breaks in realized volatility with observed long-memory properties for the daily Deutschemark/Dollar, Yen/Dollar and Yen/Deutschemark spot exchange rate realized volatility. We find that structural breaks can partly explain the persistence of realized volatility. We propose a VAR-RV-Break model that provides superior predictive ability compared to most of the forecasting models when the future break is known. With unknown break dates and sizes, we find that the VAR-RV-I(d) long memory model, however, is a very robust forecasting method even when the true financial volatility series are generated by structural breaks.

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File URL: http://faculty.washington.edu/ezivot/research/longMemoryStructuralBreaksRV.pdf
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Bibliographic Info

Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2008-20-FC.

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Date of creation: Sep 2008
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Publication status: Forthcoming in Journal of International Money and Finance
Handle: RePEc:udb:wpaper:uwec-2008-20-fc

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References

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  1. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
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  3. 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.
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  7. BAI, Jushan & PERRON, Pierre, 1998. "Computation and Analysis of Multiple Structural-Change Models," Cahiers de recherche 9807, Universite de Montreal, Departement de sciences economiques.
  8. Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2004. "‘Forecasting Time Series Subject to Multiple Structural Breaks’," Cambridge Working Papers in Economics 0433, Faculty of Economics, University of Cambridge.
  9. Choi, Kyongwook & Zivot, Eric, 2007. "Long memory and structural changes in the forward discount: An empirical investigation," Journal of International Money and Finance, Elsevier, vol. 26(3), pages 342-363, April.
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Cited by:
  1. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
  2. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, School of Economics and Management, University of Aarhus.
  3. Agata Kliber, 2013. "Influence of the Greek Crisis on the Risk Perception of European Economies," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 5(2), pages 125-161, June.
  4. WANG, Shin-Huei & BAUWENS, Luc & HSIAO, Cheng, 2012. "Forecasting long memory processes subject to structural breaks," CORE Discussion Papers 2012048, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
  6. repec:wyi:journl:002191 is not listed on IDEAS
  7. Xiao, Weilin & Zhang, Weiguo & Zhang, Xili & Chen, Xiaoyan, 2014. "The valuation of equity warrants under the fractional Vasicek process of the short-term interest rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 320-337.
  8. repec:wyi:journl:002213 is not listed on IDEAS

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