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

Author

Listed:
  • 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.

Suggested Citation

  • Francois-Éric Racicot & Raymond Théoret & Alain Coen, 2006. "Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models," RePAd Working Paper Series UQO-DSA-wp152006, Département des sciences administratives, UQO.
    • Handle: RePEc:pqs:wpaper:152006
    as

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    File URL: http://www.repad.org/ca/qc/uq/uqo/dsa/RealizedVolatilityandUHF_repad_juillet_2006.pdf
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    References listed on IDEAS

    as
    1. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    2. Tim Bollerslev & Jonathan H. Wright, 2001. "High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 596-602, November.
    3. 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.
    4. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    5. 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.
    6. repec:dau:papers:123456789/5478 is not listed on IDEAS
    7. Breidt, F. J. & Carriquiry, Alicia L., 1996. "Improved Quasi-Maximum Likelihood for Stochastic Volatility Models," Staff General Research Papers Archive 1143, Iowa State University, Department of Economics.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. François-Éric Racicot & Raymond Théoret, 2009. "Integrating volatility factors in the analysis of the hedge fund alpha puzzle," Journal of Asset Management, Palgrave Macmillan, vol. 10(1), pages 37-62, April.
    2. 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.
    3. Jean-Pierre Allegret & Cécile Couharde & Cyriac Guillaumin, 2012. "The Impact of External Shocks in East Asia: Lessons from a Structural VAR Model with Block Exogeneity," International Economics, CEPII research center, issue 132, pages 35-89.
    4. Haugom, Erik & Westgaard, Sjur & Solibakke, Per Bjarte & Lien, Gudbrand, 2011. "Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data," Energy Economics, Elsevier, vol. 33(6), pages 1206-1215.

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    More about this item

    Keywords

    Realized volatility; Ultra High Frequency GARCH; time deformation; financial markets; Daily VaR.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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