具有舍入误差微观结构噪音高频数据的杠杆效应分析

Analysis of Leverage Effect Based on High Frequency Data with Rounding Error Market Microstructure Noise

本杠杆效应反映了股票收益率与其波动率变动之间的负相关关系,它一直是金融研究的核心问题.在高频时间序列数据中,传统的简单相关系数估计是不相合的,为此一些学者给出了新的杠杆效应刻画-积分杠杆效应,并给出该杠杆效应的估计量.众所周知,高频数据易受市场微观结构噪音的干扰,其中舍入误差是非常重要、实际中普遍存在的一类.高频数据被舍入误差噪音污染后,本文研究上述学者提出的杠杆效应估计量的稳健性,获得杠杆效应估计的相合性及渐近正态性,并用随机模拟对结果进行了验证.

The negative correlations between stock returns and their volatility changes are called the leverage effect,which is a core issue in financial research.Because the common simple correlation coefficient isn’t consistent any more in the context of high frequency data,some researchers proposed a new characterization of leverage effect:the integrated leverage effect and its estimators as well.As is well-known,high frequency data are too apt to be contaminated by market microstructure noise.Rounding is a crucial source of market microstructure noise and is the common phenomena in stock returns data.Based the rounding-error-contaminated high frequency data,the paper studies the robustness of the estimator of the integrated leverage effect and deduces its consistency and asymptotic normality.Furthermore,simulations illustrate our theoretical results.