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Nonparametric estimation of the volatility under microstructure noise: wavelet adaptation

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  • Hoffmann, Marc
  • Munk, Axel
  • Schmidt-Hieber, Johannes

Abstract

We study nonparametric estimation of the volatility function of a diffusion process from discrete data, when the data are blurred by additional noise. This noise can be white or correlated, and serves as a model for microstructure effects in financial modeling, when the data are given on an intra-day scale. By developing pre-averaging techniques combined with wavelet thresholding, we construct adaptive estimators that achieve a nearly optimal rate within a large scale of smoothness constraints of Besov type. Since the underlying signal (the volatility) is genuinely random, we propose a new criterion to assess the quality of estimation; we retrieve the usual minimax theory when this approach is restricted to deterministic volatility.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 24562.

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Date of creation: 27 Jul 2010
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Handle: RePEc:pra:mprapa:24562

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Keywords: Adaptive estimation; diffusion processes; high-frequency data; microstructure noise; minimax estimation; semimartingales; wavelets.;

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