On Ornstein-Uhlenbeck driven by Ornstein-Uhlenbeck processes
Bernard Bercu, Frederic Proia, Nicolas Savy
TL;DR
This paper studies the long-term properties of maximum likelihood estimators for parameters in Ornstein-Uhlenbeck processes driven by Ornstein-Uhlenbeck noise, focusing on their asymptotic behavior.
Contribution
It provides new insights into the asymptotic properties of estimators for a class of complex stochastic processes driven by Ornstein-Uhlenbeck noise.
Findings
Establishes asymptotic normality of estimators
Derives consistency results for parameter estimation
Analyzes the impact of Ornstein-Uhlenbeck noise on estimator behavior
Abstract
We investigate the asymptotic behavior of the maximum likelihood estimators of the unknown parameters of positive recurrent Ornstein-Uhlenbeck processes driven by Ornstein-Uhlenbeck processes.
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Taxonomy
TopicsStochastic processes and financial applications · Stochastic processes and statistical mechanics · Random Matrices and Applications