Abstract:We study market microstructure noise in high-frequency data and analyze its implications for the (RV) under ageneral specification for the noise. We show that kernel-based estimators can uneh important characteristics of market microstruc-ture noise and that a simple kernel-based estimator dominates the RV for the estimation of (IV). An empirical analysis of the Dow Jones Industrial Average stocks reveals that market microstructure noise is time-dependent and correlated with increments in the efficient price. This has important implications for volatility estimation based on high-frequency data. Finally, we apply cointegration techniques to decompose transaction prices and bid-ask quotes into an estimate of the efficient price and noise. This framework enables us to study the dynamic effects on transaction prices and quotes caused by changes in the efficient price.
Keywords: Realized Variance; ; Integrated Variance; Market Microstructure Noise; Bias Correction; High-Frequency Data; Sampling Schemes.
1. Introduction
2. The Theoretical Framework
3. The Case with Independent Noise
4. The Case with Dependent Noise
5. Empirical Analysis
6. Price Decomposition by Cointegration Methods
7. Summary and Concluding Remarks
摘要：我们研究市场微观结构噪音高频数据分析（RV）在业内总的规范噪音的影响。我们表明，基于内核的估计可以uneh重要的市场微观结构噪音的特点，一个简单的内核为基础的估计占主导地位的估计（IV）的RV。道琼斯工业平均指数股票的实证分析显示，市场微观结构噪音是时间依赖性和与高效价的增量。基于高频数据的波动性估计具有重要的意义。最后，我们运用协整技术到高效价的估计和噪声，分解交易价格和买入卖出报价。这个框架，使我们能够研究高效价的变化所造成的交易价格以及报价的动态效果。
关键词：实现差异;综合方差;市场微观结构噪声偏置校正高频数据采样计划。
1。介绍
2。理论框架
3。独立噪声的情况
4。相关噪声的情况
5。实证分析
6。价格协整方法分解
7。总结和结束语
