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Volatility Estimation on the Basis of Price Intensities


  • Gerhard, Frank
  • Hautsch, Nikolaus


This paper investigates the use of price intensities to estimate volatilities based on high-frequency data. We interpret the conditional probability for the occurence of a price event within a certain time horizon as a risk measure which allows us to obtain an estimator of the conditional volatility per time. this kind of volatility estimation solves the problem of an appropriate eggregation level by defining explicitly price events. To consider grouping caused by the nontrading period overnight we use a categorical duration model. This model allows us to take into account that durations which occur overnight can only be registered by a lower and an upper bound. The use of price durations based on different tick sizes make it possible to investigate volaility patterns depending on different aggregation levels. Seasonalities are taken into account by including regressors based on a flexible Fourier form based on intraday and time to maaturity seasonalities. Testing for serial correlation and controlling for unobservable heterogeneity permits us to check for misspecification on different aggregation levels. Empirical results are based on intraday transaction data of Bund Future trading at the LIFFE in London.

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  • Gerhard, Frank & Hautsch, Nikolaus, 1999. "Volatility Estimation on the Basis of Price Intensities," CoFE Discussion Papers 99/19, University of Konstanz, Center of Finance and Econometrics (CoFE).
  • Handle: RePEc:zbw:cofedp:9919

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    Cited by:

    1. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    2. Rodríguez Poo, Juan M. & Veredas, David & Espasa, Antoni, 2001. "On the (intradaily) seasonality and dynamics of a financial point process: a semiparametric approach," DES - Working Papers. Statistics and Econometrics. WS ws013321, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 450-493.
    4. BAUWENS, Luc & HAUTSCH, Nikolaus, 2006. "Modelling financial high frequency data using point processes," CORE Discussion Papers 2006080, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Chen, Kim Heng & Jandhyala, Venkata K. & Fotopoulos, Stergios B., 2005. "Nonlinear Properties of Multifactor Financial Models," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 1(2), pages 1-27.
    6. Thierry Michel & Bertrand Maillet, 2002. "How Deep was the September 2001 Stock Market Crisis? Putting Recent Events on the American and French Markets into Perspective with an Index of Market Shocks," FMG Discussion Papers dp417, Financial Markets Group.
    7. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    8. Thierry Chauveau & Sylvain Friederich & Jérôme Héricourt & Emmanuel Jurczenko & Catherine Lubochinsky & Bertrand Maillet & Christophe Moussu & Bogdan Négréa & Hélène Raymond-Feingold, 2004. "La volatilité des marchés augmente-t-elle ?," Revue d'Économie Financière, Programme National Persée, vol. 74(1), pages 17-44.
    9. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, September.
    10. Fotopoulos, Stergios B. & Jandhyala, Venkata K. & Chen, Kim-Heng, 2007. "Non-linear properties of conditional returns under scale mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3041-3056, March.
    11. 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, June.

    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets


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