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Volatility estimation on the basis of price intensities

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  • Gerhard, Frank
  • Hautsch, Nikolaus

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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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 9 (2002)
Issue (Month): 1 (January)
Pages: 57-89

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Handle: RePEc:eee:empfin:v:9:y:2002:i:1:p:57-89

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Cited by:
  1. 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.
  2. 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.
  3. 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).
  4. GIOT, Pierre & LAURENT, Sébastien, . "Modelling daily Value-at-Risk using realized volatility and ARCH type models," CORE Discussion Papers RP -1708, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. 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.
  6. 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).
  7. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
  8. 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.
  9. 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.
  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.

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