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.
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.
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].
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