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arXiv · 1809.02360

Asymptotic efficiency for covariance estimation under noise and asynchronicity

Sebastian Holtz
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Abstract

The estimation of the covariance structure from a discretely observed multivariate Gaussian process under asynchronicity and noise is analysed under high-frequency asymptotics. Asymptotic lower and upper bounds are established for a general Gaussian framework which provides benchmark cases for various Gaussian process models of interest. The parametric bounds give rise to infinite-dimensional convolution theorems for covariation estimation under asynchronicity, which is an essential estimation problem in finance.

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Sebastian Holtz. 2018-09-07. Asymptotic efficiency for covariance estimation under noise and asynchronicity. https://arxiv.org/abs/1809.02360

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