Least Squares Estimation for SDEs with Lévy Noise and Sparse Data
Research Paper#Stochastic Differential Equations, Lévy Noise, Least Squares Estimation, Sparse Data🔬 Research|Analyzed: Jan 3, 2026 18:21•
Published: Dec 30, 2025 05:58
•1 min read
•ArXivAnalysis
This paper addresses a practical problem in financial modeling and other fields where data is often sparse and noisy. The focus on least squares estimation for SDEs perturbed by Lévy noise, particularly with sparse sample paths, is significant because it provides a method to estimate parameters when data availability is limited. The derivation of estimators and the establishment of convergence rates are important contributions. The application to a benchmark dataset and simulation study further validate the methodology.
Key Takeaways
- •Provides least squares estimators for SDEs with Lévy noise.
- •Addresses the challenge of sparse data in parameter estimation.
- •Establishes asymptotic convergence rates for the estimators.
- •Validates the methodology with a benchmark dataset and simulations.
Reference / Citation
View Original"The paper derives least squares estimators for the drift, diffusion, and jump-diffusion coefficients and establishes their asymptotic rate of convergence."