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Adaptive Realized Kernels

  • Marine Carrasco

    (Université de Montréal, Départment d'Economie - CIREQ - Centre interuniversitaire de recherche en économie quantitative - Université de Montréal)

  • Rachidi Kotchoni


    (THEMA - Théorie économique, modélisation et applications - Université de Cergy Pontoise - CNRS)

We design adaptive realized kernels to estimate the integrated volatility in a framework that combines a stochastic volatility model with leverage effect for the efficient price and a semiparametric microstructure noise model speci ed at the highest frequency. Some time dependence parameters of the noise model must be estimated before adaptive realized kernels can be implemented. We study their performance by simulation and illustrate their use with twelve stocks listed in the Dow Jones Industrial. As expected, we nd that adaptive realized kernels achieves the optimal trade-off between the discretization error and the microstructure noise.

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Paper provided by HAL in its series Working Papers with number hal-00867967.

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Date of creation: 30 Sep 2013
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Handle: RePEc:hal:wpaper:hal-00867967
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  1. Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
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  12. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank, Research Centre.
  13. Bernard Bollen & Brett Inder, 1999. "Estimating Daily Volatility in Financial Markets Utilizing Intraday Data," Working Papers 1999.01, School of Economics, La Trobe University.
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  18. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
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