English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper discusses the local linear smoothing to estimate the unknown firstand second infinitesimal moments in second-order jump-diffusion model based onGamma asymmetric kernels. Under the mild conditions, we obtain the weakconsistency and the asymptotic normality of these estimators for both interiorand boundary design points. Besides the standard properties of the local linearestimation such as simple bias representation and boundary bias correction, thelocal linear smoothing using Gamma asymmetric kernels possess some extraadvantages such as variable bandwidth, variance reduction and resistance tosparse design, which is validated through finite sample simulation study.Finally, we employ the estimators for the return of some high frequencyfinancial data.

Local Nonparametric Estimation for Second-Order Jump-Diffusion Model Using Gamma Asymmetric Kernels