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Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models

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  • Francois-Éric Racicot

    ()
    (Département des sciences administratives, Université du Québec (Outaouais) et LRSP)

  • Raymond Théoret

    ()
    (Département de stratégie des affaires, Université du Québec (Montréal))

  • Alain Coen

    ()
    (Département de stratégie des affaires, Université du Québec (Montréal))

Abstract

A very promising literature has been recently devoted to the modeling of ultra-high-frequency (UHF) data. Our first aim is to develop an empirical application of Autoregressive Conditional Duration GARCH models and the realized volatility to forecast future volatilities on irregularly spaced data. We also compare the out sample performances of ACD GARCH models with the realized volatility method. We propose a procedure to take into account the time deformation and show how to use these models for computing daily VaR.

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Bibliographic Info

Paper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number UQO-DSA-wp152006.

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Length: 27 pages
Date of creation: 06 Jul 2006
Date of revision:
Handle: RePEc:pqs:wpaper:152006

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Keywords: Realized volatility; Ultra High Frequency GARCH; time deformation; financial markets; Daily VaR.;

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References

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  1. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
  2. Jonathan H. Wright & Tim Bollerslev, 1999. "High frequency data, frequency domain inference and volatility forecasting," International Finance Discussion Papers 649, Board of Governors of the Federal Reserve System (U.S.).
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  9. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  10. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
  11. Robert F. Engle, 1996. "The Econometrics of Ultra-High Frequency Data," NBER Working Papers 5816, National Bureau of Economic Research, Inc.
  12. Bollerslev, Tim & Zhang, Benjamin Y. B., 2003. "Measuring and modeling systematic risk in factor pricing models using high-frequency data," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 533-558, December.
  13. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 1-46 National Bureau of Economic Research, Inc.
  14. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  15. Ole E. Barndorff-Nielsen & Neil Shephard, 2000. "Econometric analysis of realised volatility and its use in estimating stochastic volatility models," Economics Papers 2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
  16. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
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Cited by:
  1. Josifidis, Kosta & Allegret, Jean-Pierre & Gimet, Céline & Pucar, Emilija Beker, 2014. "Macroeconomic policy responses to financial crises in emerging European economies," Economic Modelling, Elsevier, vol. 36(C), pages 577-591.

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