The Explicative Market Microstructure Noise

• 成果类型: Article; Early Access
• 署名作者: Cui, Wenhao
• 署名单位: Beihang University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2622104
• 发表日期: 2026-03-23
• 关键词: High-frequency financial data; Market microstructure noise; Trading information; variable importance; INTEGRATED VOLATILITY; efficient estimation; heteroskedasticity; limit

摘要

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the identified significant variables, we then introduce a nonparametric estimator for the explicative noise and establish its asymptotic properties. The finite-sample performance of the proposed methods is assessed through Monte Carlo simulations calibrated to real data. Finally, an empirical application shows that the explicative noise component plays a key role in explaining return variation, and that accounting for it substantially smooths the volatility signature curve. Supplementary materials for this article are available online.