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Main Authors: Yan, Chengxin, Chen, Dachuan, Li, Jia
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2601.18371
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author Yan, Chengxin
Chen, Dachuan
Li, Jia
author_facet Yan, Chengxin
Chen, Dachuan
Li, Jia
contents We provide a comprehensive analysis of spot volatility inference in pure-jump semimartingales under two asymptotic settings: fixed-$k$, where each local window uses a fixed number of observations, and large-$k$, where this number grows with sampling frequency. For both active- and possibly inactive-jump settings, we derive generally nonstandard, typically non-Gaussian limit distributions and establish valid inference, including when the jump-activity index is consistently estimated. Simulations show that fixed-$k$ asymptotics offer markedly better finite-sample accuracy, underscoring their practical advantage for nonparametric spot volatility inference.
format Preprint
id arxiv_https___arxiv_org_abs_2601_18371
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Nonparametric inference for spot volatility in pure-jump semimartingales
Yan, Chengxin
Chen, Dachuan
Li, Jia
Statistics Theory
We provide a comprehensive analysis of spot volatility inference in pure-jump semimartingales under two asymptotic settings: fixed-$k$, where each local window uses a fixed number of observations, and large-$k$, where this number grows with sampling frequency. For both active- and possibly inactive-jump settings, we derive generally nonstandard, typically non-Gaussian limit distributions and establish valid inference, including when the jump-activity index is consistently estimated. Simulations show that fixed-$k$ asymptotics offer markedly better finite-sample accuracy, underscoring their practical advantage for nonparametric spot volatility inference.
title Nonparametric inference for spot volatility in pure-jump semimartingales
topic Statistics Theory
url https://arxiv.org/abs/2601.18371