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Volatility Estimation on the Basis of Price Intensities

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  • Frank Gerhard

    ()
    (Center of Finance and Econometrics)

  • Nikolaus Hautsch

    ()
    (Center of Finance and Econometrics)

Abstract

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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Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 99-19.

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Length: 44 pages
Date of creation: 20 Oct 1999
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Handle: RePEc:knz:cofedp:9919

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Citations

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Cited by:
  1. Nikolaus Hautsch, 2002. "Modelling Intraday Trading Activity Using Box-Cox-ACD Models," CoFE Discussion Paper 02-05, Center of Finance and Econometrics, University of Konstanz.
  2. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
  3. Pierre Giot & Sébastien Laurent, 2002. "Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models," Computing in Economics and Finance 2002 52, Society for Computational Economics.
  4. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
  5. David Veredas & Juan M. Rodríguez-Poo & Antoni Espasa, 2001. "On The (Intradaily) Seasonality And Dynamics Of A Financial Point Process: A Semiparametric Approach," Statistics and Econometrics Working Papers ws013321, Universidad Carlos III, Departamento de Estadística y Econometría.
  6. Hélène Raymond-Feingold & Bogdan Négréa & Christophe Moussu & Bertrand Maillet & Catherine Lubochinsky & Emmanuel Jurczenko & Jérôme Héricourt & Sylvain Friederich & Thierry Chauveau, 2004. "La volatilité des marchés augmente-t-elle ?," Revue d'Économie Financière, Programme National Persée, vol. 74(1), pages 17-44.
  7. 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.
  8. 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.
  9. Chen, Kim Heng & Jandhyala, Venkata K. & Fotopoulos, Stergios B., 2005. "Nonlinear Properties of Multifactor Financial Models," Review of Applied Economics, Review of Applied Economics, vol. 1(2).
  10. 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, 09.

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