Johannes Schmidt-Hieber

  1. Multiscale Methods for Shape Constraints in Deconvolution.

    Authors: Axel Munk, Johannes Schmidt-Hieber, Lutz Duembgen
    Subjects: Statistics
    Abstract

    We derive multiscale statistics for deconvolution in order to detect
    qualitative features of the unknown density. An important example covered
    within this framework is to test for local monotonicity on all scales
    simultaneously. The errors in the deconvolution model are restricted to a
    certain class of distributions that include Laplace, Gamma and Exponential
    random variables. Our approach relies on inversion formulas for deconvolution
    operators. For multiscale testing, we consider a calibration, motivated by the
    modulus of continuity of Brownian motion.

  2. Nonparametric estimation of the volatility under microstructure noise: wavelet adaptation.

    Authors: Axel Munk, Johannes Schmidt-Hieber, Marc Hoffmann
    Subjects: Statistics
    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.

  3. Lower bounds for volatility estimation in microstructure noise models.

    Authors: Axel Munk, Johannes Schmidt-Hieber
    Subjects: Statistics
    Abstract

    In this paper we derive lower bounds in minimax sense for estimation of the
    instantaneous volatility if the diffusion type part cannot be observed directly
    but under some additional Gaussian noise. Three different models are
    considered. Our technique is based on a general inequality for Kullback-Leibler
    divergence of multivariate normal random variables and spectral analysis of the
    processes. The derived lower bounds are indeed optimal. Upper bounds can be
    found in Munk and Schmidt-Hieber [18].

  4. Nonparametric estimation of the volatility function in a high-frequency model corrupted by noise.

    Authors: Axel Munk, Johannes Schmidt-Hieber
    Subjects: Methodology
    Abstract

    We consider the models Y_{i,n}=\int_0^{i/n}
    \sigma(s)dW_s+\tau(i/n)\epsilon_{i,n}, and \tilde
    Y_{i,n}=\sigma(i/n)W_{i/n}+\tau(i/n)\epsilon_{i,n}, i=1,...,n, where W_t
    denotes a standard Brownian motion and \epsilon_{i,n} are centered i.i.d.
    random variables with E(\epsilon_{i,n}^2)=1 and finite fourth moment.
    Furthermore, \sigma and \tau are unknown deterministic functions and W_t and
    (\epsilon_{1,n},...,\epsilon_{n,n}) are assumed to be independent processes.
    Based on a spectral decomposition of the covariance structures we derive series
    estimators for \sigma^2 and \tau^2 and investigate t

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