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Time transformations, intraday data, and volatility models

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  • GIOT, Pierre

Abstract

In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE.We present two different frameworks for analyzing this dataset. First, using regularly sampled observations, we characterize the intraday volatility of the mid-point of the bid-ask quotes by estimating GARCH and EGARCH models, with intraday seasonalitybeing accounted for. We also highlight the impact of characteristics of the trade process (traded volume, number of trades and average volume per trade) on the volatility specifications. Secondly, we deal directly with the irregularly spaced data. We review two time transformations that allowa thinning of the original dataset such that new durations are defined. The newly defined price and volume durations are characterized and the performance of the Log-ACD model for modelling these durations is assessed. Moreover, price durations allowan easy computation of intraday volatility and this method compares favorablyto ARCH estimations.
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  • GIOT, Pierre, 2001. "Time transformations, intraday data, and volatility models," LIDAM Reprints CORE 1500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1500
    Note: In : Journal of Computational Finance, 4(2), 31-62, 2001
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    Cited by:

    1. Bauwens, Luc & Giot, Pierre & Grammig, Joachim & Veredas, David, 2004. "A comparison of financial duration models via density forecasts," International Journal of Forecasting, Elsevier, vol. 20(4), pages 589-609.
    2. DOLADO , Juan J. & RODRIGUEZ-POO, Juan & VEREDAS, David, 2004. "Testing weak exogeneity in the exponential family : an application to financial point processes," LIDAM Discussion Papers CORE 2004049, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Filip Zikes & Vít Bubák, 2006. "Trading Intensity and Intraday Volatility on the Prague Stock Exchange: Evidence from an Autoregressive Conditional Duration Model (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(5-6), pages 223-245, May.
    4. Pierre Giot & Joachim Grammig, 2006. "How large is liquidity risk in an automated auction market?," Empirical Economics, Springer, vol. 30(4), pages 867-887, January.
    5. Fernandes, Marcelo & Grammig, Joachim, 2006. "A family of autoregressive conditional duration models," Journal of Econometrics, Elsevier, vol. 130(1), pages 1-23, January.
    6. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    7. Xiaodong Jin & Janusz Kawczak, 2003. "Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 103-124, May.
    8. Fatima Sol Murta, 2007. "The Money Market Daily Session :an UHF-GARCH Model Applied to the Portuguese Case Before and After the Introduction Of the Minimum Reserve System of the Single Monetary Policy," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 50(3), pages 285-314.
    9. Takayuki Morimoto, 2004. "Estimating and forecasting instantaneous volatility through a duration model : An assessment based on VaR," Econometric Society 2004 Far Eastern Meetings 592, Econometric Society.
    10. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    11. Chu, Carlin C.F. & Lam, K.P., 2011. "Modeling intraday volatility: A new consideration," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(3), pages 388-418, July.
    12. Steland, Ansgar, 2004. "NP-optimal kernels for nonparametric sequential detection rules," Technical Reports 2004,09, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    13. Matos, João Manuel Gonçalves Amaro de & Fernandes, Marcelo, 2001. "Testing the Markov property with ultra high frequency financial data," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 414, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    14. Magdalena Osinska & Andrzej Dobrzynski & Yochanan Shachmurove, 2016. "Performance Of American And Russian Joint Stock Companies On Financial Market. A Microstructure Perspective," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 819-851, December.
    15. GIOT, Pierre, 2000. "Intraday value-at-risk," LIDAM Discussion Papers CORE 2000045, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. Pipat Wongsaart & Jiti Gao, 2011. "Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 18/11, Monash University, Department of Econometrics and Business Statistics.
    17. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    18. Katarzyna Bien-Barkowska, 2011. "Distribution Choice for the Asymmetric ACD Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 55-72.

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