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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
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    References listed on IDEAS

    as
    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 335-356, December.
    3. 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 3-46, National Bureau of Economic Research, Inc.
    4. 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.
    5. 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.
    6. 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.
    7. Gourieroux, Christian & Jasiak, Joanna & Le Fol, Gaelle, 1999. "Intra-day market activity," Journal of Financial Markets, Elsevier, vol. 2(3), pages 193-226, August.
    8. 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.
    9. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    10. 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.
    11. Ole E. Barndorff‐Nielsen & Neil 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, May.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. 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.
    14. Meddahi, N., 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche 2001-26, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    15. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    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.
    17. 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.
    18. repec:dau:papers:123456789/5478 is not listed on IDEAS
    19. Carriquiry, Alicia L. & Breidt, F. J., 1996. "Improved Quasi-Maximum Likelihood for Stochastic Volatility Models," Staff General Research Papers Archive 1035, Iowa State University, Department of Economics.
    20. 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.
    21. 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.
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    Cited by:

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    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. Montero, José-María & Naimy, Viviane & Farraj, Nermeen Abi & El Khoury, Rim, 2024. "Natural disasters, stock price volatility in the property-liability insurance market and sustainability: An unexplored link," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    5. 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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