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Low-rank diffusion matrix estimation for high-dimensional time-changed Levy processes

Annales de l'institut Henri Poincare (B) Probability and Statistics. 2018. Vol. 54. No. 3. P. 1583–1621.
Belomestny D., Trabs M.

The estimation of the diffusion matrix Σ of a high-dimensional, possibly time-changed Levy process is studied, based on discrete observations of the process with a fixed distance. A low-rank condition is imposed on Σ. Applying a spectral approach, we construct a weighted least-squares estimator with nuclear-norm-penalisation. We prove oracle inequalities and derive convergence rates for the diffusion matrix estimator. The convergence rates show a surprising dependency on the rank of Σ and are optimal in the minimax sense for fixed dimensions. Theoretical results are illustrated by a simulation study.

Research target: Mathematics
Priority areas: IT and mathematics
Language: English
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DOI
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Keywords: volatility estimatorsLevy processes
Publication based on the results of:
Стохастические алгоритмы и статистический анализ многомерных данных (2018)
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