与低频数据不同，高频数据通常具有“日历效应”和波动长记忆性。
Unlike low frequency data, high frequency data has the calendar effects and long memory volatility.
本文从定义形式、无偏性、有效性、日历效应等方面对已实现波动和赋权已实现波动加以比较。
In this paper, we compare realized volatility and weighted realized volatility from four aspects: defnition, bias, efficiency and calendar effect.
日历效应是指证券市场出现的在某一特定时间进行交易可以获得超额收益率的现象，它的表现形式主要有星期效应和月份效应。
Calendar effects mean that market returns associate with the specific transaction date in stock market, there re two important forms: day of the week effects and month of the year effects.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
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