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

  • 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))

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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File URL: http://www.repad.org/ca/qc/uq/uqo/dsa/RealizedVolatilityandUHF_repad_juillet_2006.pdf
File Function: First version, 2006
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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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  1. 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.
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  9. Neil Shephard & Ole E. Barndorff-Nielsen, 2002. "Estimating quadratic variation using realised variance," Economics Series Working Papers 2001-W20, University of Oxford, Department of Economics.
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  13. Ole E. Barndorff-Nielsen & Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280.
  14. 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.
  15. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
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