The Explicative Market Microstructure Noise
研究了高频数据中可由交易信息解释的微观结构噪声部分，提出了无模型变量重要性度量和非参数估计方法，实证表明该噪声成分能解释收益变化并平滑波动率曲线。
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.