Efficient estimation of jump parameters for stochastic differential equations driven by L{é}vy processes

Fuente: arXiv
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Autores principales: Bayraktar, Elise, Clément, Emmanuelle
Formato: Preprint
Publicado: 2025
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author Bayraktar, Elise
Clément, Emmanuelle
author_facet Bayraktar, Elise
Clément, Emmanuelle
contents In a high-frequency context, we investigate the efficient estimation of scaling and jump activity parameters for a stochastic differential equation driven by a L{é}vy process with both diffusion component and pure-jump component. We first study efficiency for the prototype L{é}vy process. With an in-depth analysis of the behavior of the density of the process in small time, we prove that the LAN Property holds for the joint estimation of the diffusion, scaling and jump activity parameters. We next consider a stochastic equation driven both by a Brownian Motion and a locally stable pure-jump L{é}vy process. Using a quasi-likelihood estimation method, we exhibit an estimator that attains the optimal rate of convergence previously identified. The asymptotic properties of the estimator are derived from sharp approximation results for the stochastic equation and for the locally stable distribution.
format Preprint
id arxiv_https___arxiv_org_abs_2509_04920
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Efficient estimation of jump parameters for stochastic differential equations driven by L{é}vy processes
Bayraktar, Elise
Clément, Emmanuelle
Probability
In a high-frequency context, we investigate the efficient estimation of scaling and jump activity parameters for a stochastic differential equation driven by a L{é}vy process with both diffusion component and pure-jump component. We first study efficiency for the prototype L{é}vy process. With an in-depth analysis of the behavior of the density of the process in small time, we prove that the LAN Property holds for the joint estimation of the diffusion, scaling and jump activity parameters. We next consider a stochastic equation driven both by a Brownian Motion and a locally stable pure-jump L{é}vy process. Using a quasi-likelihood estimation method, we exhibit an estimator that attains the optimal rate of convergence previously identified. The asymptotic properties of the estimator are derived from sharp approximation results for the stochastic equation and for the locally stable distribution.
title Efficient estimation of jump parameters for stochastic differential equations driven by L{é}vy processes
topic Probability
url https://arxiv.org/abs/2509.04920