Liquidity—How to Capture a Multidimensional Beast

Philip Sommer, S. Pasquali
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引用次数: 7

Abstract

Despite its importance, there currently exists no universally agreed upon and adopted measure or model that adequately captures cost and time to liquidation in bond (over-thecounter) markets. To fill this gap, we reviewed 40 years’ worth of research and summarize our findings in this article. We claim that the lack of concurrence on a definition can be attributed to the lack of consistent methodology. Connecting the dots within the vast body of literature, we find the key ingredients of such a novel measure: Taking market impact models as a natural starting point and adding the necessary math to quantify the inherent uncertainty of such a measure. We further suggest that machine learning methods are a natural candidate to overcome the main obstacles in this process, as they can help extract useful information from the extremely sparse data that form the main difference between equity and bond markets.
流动性——如何捕捉多维野兽
尽管它很重要,但目前还没有普遍认可和采用的措施或模型来充分反映债券(场外交易)市场清算的成本和时间。为了填补这一空白,我们回顾了40年来的研究成果,并在本文中总结了我们的发现。我们认为,在定义上缺乏一致性可归因于缺乏一致的方法。将大量文献中的点连接起来,我们发现了这种新措施的关键成分:将市场影响模型作为自然起点,并添加必要的数学来量化这种措施的内在不确定性。我们进一步建议,机器学习方法是克服这一过程中主要障碍的自然候选者,因为它们可以帮助从极其稀疏的数据中提取有用的信息,这些数据构成了股票和债券市场之间的主要差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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