交易活动的量纲分析与实证标度定律
文章 arXiv papers · 作者: Mathias Pohl et al.
总结
本文研究交易笔数、成交量、价格、波动率、价差和交易成本之间的关系。研究使用量纲分析,说明这些变量的特定组合可能具有哪些比例关系。相关关系包括交易笔数与波动率平方成比例、涉及价格、成交量、波动率和成本的 3/2 次幂标度关系,以及波动率、价格和价差之间的平方标度关系。
作者使用NASDAQ股票数据检验了较复杂的关系,并报告称获得了具有一定普遍性的实证支持。他们还讨论了波动率随时间变化的标度特征,并指出这种行为比简单假设所暗示的更为微妙。摘要没有提供效应大小或详细的检验程序;报告的关系应结合研究考察的变量组合和证据来理解。
核心观点
- 交易笔数、成交量、价格、波动率、价差和成本可以通过量纲标度关系联系起来。
- 量纲分析可识别特定变量组合可能具有的标度定律形式。
- 本文考察了联系交易活动、波动率、价格、成交量和成本的 3/2 次幂关系。
- NASDAQ数据为较复杂的标度关系提供了具有一定普遍性的实证支持。
- 波动率的时间标度需要更谨慎地分析,不能简单套用直观假设。
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# Theoretical and empirical analysis of trading activity
# Theoretical and empirical analysis of trading activity
Understanding the structure of financial markets deals with suitably determining the functional relation between financial variables. In this respect, important variables are the trading activity, defined here as the number of trades $N$, the traded volume $V$, the asset price $P$, the squared volatility $σ^2$, the bid-ask spread $S$ and the cost of trading $C$. Different reasonings result in simple proportionality relations ("scaling laws") between these variables. A basic proportionality is established between the trading activity and the squared volatility, i.e., $N \sim σ^2$. More sophisticated relations are the so called 3/2-law $N^{3/2} \sim σP V /C$ and the intriguing scaling $N \sim (σP/S)^2$. We prove that these "scaling laws" are the only possible relations for considered sets of variables by means of a well-known argument from physics: dimensional analysis. Moreover, we provide empirical evidence based on data from the NASDAQ stock exchange showing that the sophisticated relations hold with a certain degree of universality. Finally, we discuss the time scaling of the volatility $σ$, which turns out to be more subtle than one might naively expect.在遵守原作品许可的前提下,附作者信息全文展示。 许可协议: abstract CC0
此摘要由 Stratmill 研究智能体根据原文撰写,并非原文副本。