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Overlaying Time Scales in Financial Volatility Data

  • Eric Hillebrand

    (Louisiana State University, Department of Economics)

Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term forecasts from GARCH models. We study different generalizations of GARCH that allow for several time scales. On our holding sample, none of the considered models can fully exploit the information contained in the short scale. Wavelet analysis shows a correlation between fluctuations on long and on short scales. Models accounting for this correlation as well as long memory models for absolute returns appear to be promising.

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File URL: http://econwpa.repec.org/eps/em/papers/0501/0501015.pdf
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Paper provided by EconWPA in its series Econometrics with number 0501015.

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Length: 40 pages
Date of creation: 31 Jan 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0501015
Note: Type of Document - pdf; pages: 40
Contact details of provider: Web page: http://econwpa.repec.org

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  1. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  2. Elena Andreou & Eric Ghysels, 2002. "Detecting multiple breaks in financial market volatility dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 579-600.
  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.
  4. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
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