基于高频数据的波动率矩阵估计可有效解决传统低频估计面临的种种瓶颈问题。然而,由于受非同步和微观结构噪声等的影响,传统的高频波动率矩阵估计会产生艾普斯效应,并偏离其理论值。本文主要考虑非同步逐笔高频数据的三种同步化方法和五种传统已实现波动率矩阵的纠偏降噪方法,并从数值模拟和沪深股市的实证分析两个角度对两类方法分别展开了全面深入的比较研究。结果表明:更新时间同步化法最大程度地保留了数据信息,传统未纠偏的已实现波动率矩阵具有艾普斯效应,其偏差较大,多变量已实现核估计、双频已实现波动率矩阵估计、调整的已实现波动率矩阵估计的纠偏降噪效果较好,事先平均HY估计和HY估计相对表现较差。研究结果可为相关领域工作者进一步的研究与应用提供方法上的参考与指导。
High-frequency volatility matrix estimator can effectively solve some bottleneck problems faced by traditional low-frequency estimators. However, because of the influence of non-synchronous trading and market microstructure noise, it has epps effect and some big bias. So mainly three kinds of synchronization methods for non-synchronous step-by-step high-frequency data and five types of the noised-reduction methods for the traditional realized volatility matrix are considered in this paper. The two kinds of methods are deeply compared separately, from data simulation and empirical analysis. The results suggest that refresh time method includes the largest amount of data among methods we considered, realized volatility matrix has epps effect and serious bias, multivariate realized kernels, two scales realized volatility matrix estimator and modulated realized volatility matrix estimator effectively reduce noise, but pre-averaging HY and HY estimators behave a little bad. The research results can provide a useful reference and guidance on methods for workers in related fields of further research and application.
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