IDEAS home Printed from https://ideas.repec.org/p/pqs/wpaper/152006.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: http://www.repad.org/ca/qc/uq/uqo/dsa/RealizedVolatilityandUHF_repad_juillet_2006.pdf
    File Function: First version, 2006
    Download Restriction: no
    ---><---

    Other versions of this item:

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    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. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:kap:iaecre:v:14:y:2008:i:1:p:112-124 is not listed on IDEAS
    2. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 335-356, December.
    3. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    4. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2004. "Analytical Evaluation Of Volatility Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(4), pages 1079-1110, November.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    6. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    7. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    8. Barndorff-Nielsen, Ole E. & Shephard, Neil, 2006. "Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 217-252.
    9. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    11. Torben G. ANDERSEN & Tim BOLLERSLEV & Nour MEDDAHI, 2002. "Correcting The Errors : A Note On Volatility Forecast Evaluation Based On High-Frequency Data And Realized Volatilities," Cahiers de recherche 21-2002, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    12. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    13. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics," Economics Papers 2002-W13, Economics Group, Nuffield College, University of Oxford, revised 18 Mar 2002.
    14. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    15. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," Center for Financial Institutions Working Papers 02-27, Wharton School Center for Financial Institutions, University of Pennsylvania.
    16. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    17. Tauchen, George & Zhou, Hao, 2011. "Realized jumps on financial markets and predicting credit spreads," Journal of Econometrics, Elsevier, vol. 160(1), pages 102-118, January.
    18. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    19. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    20. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy (IfW Kiel).
    21. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pqs:wpaper:152006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christian Calmes (email available below). General contact details of provider: https://edirc.repec.org/data/dsuqoca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.