Some Reflections on Analysis of High-Frequency Data
高频数据分析的若干思考AI 解读 ↓
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高频数据分析的若干思考
- 研究问题
- 高频报价与成交记录除了揭示市场微观结构，还能如何帮助理解较低频率的资产收益分布？文章将讨论重点放在后一问题。
- 方法与思路
- 作者以简短评论讨论高频资料的用途，并梳理随机到达间隔、离散价格网格及丰富条件变量给计量分析带来的困难。
- 材料陈述的贡献
- 摘要强调高频数据对理解低频投机性收益分布的潜在价值，并指出如何联合处理多种高频数据特征仍是开放问题。
- 阅读关注点
- 应核对全文如何把高频价格信息联系到低频收益分布，以及这种联系需要哪些条件、统计量与数据处理步骤。
- 阅读边界
- 摘要主要交代讨论背景与关注方向，未说明具体估计方法、理论结果或实证证据，不能据此认定该用途已获验证。
原文摘要
Finance is arguably the most empirically oriented of all the social sciences. This is in part due to the deliberate practical orientation and the ready availability of high-quality financial market data. In recent years, the ever lower costs of data recording and storage have driven the phenomenon to the ultimate limit for some markets: We may have access to time-stamped observations on all quotes and transactions, denoted ultra-high-frequency data by Engle (in press). These data are of direct interest for market microstructure issues dealing with the price discovery process, the market infrastructure, the strategic behavior of market participants, and the modeling of real-time market dynamics. Moreover, the advent of ultra-high-frequency data poses interesting challenges to empirical work, and it has inspired the developments of new econometric and statistical tools dealing with such complications as a random daily number of time series observations with a random time between arrivals. In addition, the discrete price grid can distort inference concerning the asset price dynamics at the highest frequencies. Finally, any price series now has an associated string of natural conditioning variables-for example, volume, news, time of day, time between transactions or quotes, concurrent prices of similar assets, and so forth. Although current work is making strides on all these fronts, how to deal with the majority of these jointly is still an open question. Interesting surveys of the literature were provided by Hasbrouck (1996), Goodhart and O'Hara (1997), and Engle (in press). As such, the ability of high-frequency data to provide information about market microstructure issues is well recognized. Because the other participants in this discussion undoubtedly will discuss these developments, I shall instead concentrate my brief remarks on a different use of highfrequency data that is perhaps, as of yet, less appreciated-namely, the ability of such data to shed new light on issues concerning the distribution of lower-frequency speculative returns.