A least square-type procedure for parameter estimation in stochastic differential equations with additive fractional noise

By Andreas Neuenkirch, Samy Tindel. 2011

55 citations recorded by the source corpus.

We study a least square-type estimator for an unknown parameter in the drift coefficient of a stochastic differential equation with additive fractional noise of Hurst parameter $$H>1/2$$H>1/2. The estimator is based on discrete time observations of the stochastic differential equation, and using tools from ergodic theory and stochastic analysis we derive its strong consistency.

Subfields: Control and Systems Engineering, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ccaed73b9127142900c423142d54cf756aa6c66e

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In Control and Systems Engineering

In Finance

In Statistics and Probability

Citations in the corpus

Optimal rates for parameter estimation of stationary Gaussian processes (2016, methodology): “In the ergodic case, the statistical inference for several fractional Ornstein-Uhlenbeck (fOU) models via LS methods was recently developed in the papers [14], [1], [2], [13], [15], [6], [21].”
Parameter estimation for a partially observed Ornstein–Uhlenbeck process with long-memory noise (2015, background): “The LSE has also given rise to a number of successful studies in the univariate case with fractional processes: we have already cited [3, 7, 11, 20], while [2] is the continuous-time version of [3].”