标题： 高频数据的多尺度推断 显示英文标题

标题： Multiscale Inference for High-Frequency Data

摘要： 本文提出了一种新的多重尺度估计器，用于伊藤过程的积分波动率，在市场微观结构噪声（观测误差）存在的情况下。观测过程的多重尺度结构按频率表示，引入了多重尺度比率的概念，以量化由于观测误差导致的真实积分波动率中的偏差。多重尺度比率从单一样本路径估计，同时提出了一种按频率进行的偏差校正程序，该程序能够同时减少方差。我们将这种方法扩展到包含相关观测误差的情况，并提供了估计过程的时间域形式。新方法被应用于估计Heston模型以及其他模型的积分波动率，通过模拟研究展示了我们方法相对于现有方法的改进性能。 显示英文摘要

摘要： This paper proposes a novel multiscale estimator for the integrated volatility of an Ito process, in the presence of market microstructure noise (observation error). The multiscale structure of the observed process is represented frequency-by-frequency and the concept of the multiscale ratio is introduced to quantify the bias in the realized integrated volatility due to the observation error. The multiscale ratio is estimated from a single sample path, and a frequency-by-frequency bias correction procedure is proposed, which simultaneously reduces variance. We extend the method to include correlated observation errors and provide the implied time domain form of the estimation procedure. The new method is implemented to estimate the integrated volatility for the Heston and other models, and the improved performance of our method over existing methods is illustrated by simulation studies.