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Adaptive Realized Kernels

Author

Listed:
  • Marine Carrasco
  • Rachidi Kotchoni

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

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 specified 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 find that adaptive realized kernels achieves the optimal trade-off between the discretization error and the microstructure noise.

Suggested Citation

  • Marine Carrasco & Rachidi Kotchoni, 2014. "Adaptive Realized Kernels," Post-Print hal-01386059, HAL.
    • Handle: RePEc:hal:journl:hal-01386059
    DOI: 10.1093/jjfinec/nbu015
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    Cited by:

    1. Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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