High‐Frequency Data
High‐Frequency Data
复制标题
高频数据
DOI：
10.1002/9780470061602.eqf18009
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发表时间：
2010
影响因子：
8.2
通讯作者：
S. Miccichè
中科院分区：
文献类型：
--
作者：
F. Lillo;S. Miccichè
我们将介绍一些最常见的高频金融数据类型：实时数据、交易和报价数据、订单簿数据和市场成员数据。我们描述了在最流行的高频金融数据库中通常可用的变量类型。我们将讨论与处理这些数据相关的问题，包括清理协议、时间问题和与数据大小相关的问题。然后，我们简要地考虑与高频数据的实证分析中发现的风格化事实有关的问题。具体地说，我们考虑了(I)高频事件的不规则时间间隔及其与金融变量计量模型的相关性，(Ii)被调查金融变量的离散性，(Iii)与金融变量的正确定义有关的问题，(Iv)它们的日周期性，即典型的日内模式，(V)它们的时间相关性，例如买卖反弹和长记忆特性，以及(Vi)与市场结构和规则的特殊性有关的问题。
关键词：
金融市场；
高频数据；
数据分析；
一步一个脚印的数据；
订单簿数据；
市场微观结构；
经济物理学
We introduce some of the most common types of high-frequency financial data: tick-by-tick data, trade and quote data, order book data, and market member data. We describe the types of variables that are usually available in the most popular high-frequency financial databases. We discuss the issues related to the handling of these data, including cleaning protocols, timing issues, and issues related to data size. We then briefly consider the issues related to the stylized facts detected in the empirical analysis of high-frequency data. Specifically, we consider (i) the irregular temporal spacing of the events at high frequency and its relevance for the econometric modeling of financial variables, (ii) the discreteness of the financial variables under investigation, (iii) the problems related to proper definition of financial variables, (iv) their daily periodicity, that is, typical intraday patterns, (v) their temporal correlations, for example, the bid–ask bounce and long memory properties, and (vi) problems related to the specificity of the market structure and rules.
Keywords:
financial markets;
high-frequency data;
data analysis;
tick-by-tick data;
order book data;
market microstructure;
econophysics