流动性不确定性溢价之谜

IF 1 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Time Series Analysis Pub Date : 2024-12-01 DOI:10.1111/jtsa.12802

Maria Flora, Ilaria Gianstefani, Roberto Renò

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引用次数: 0

摘要

流动性不确定性与资产收益之间令人费解的负相关关系最初由Chordia、Subrahmanyam和Anshuman（2001）提出，并得到后续实证文献的证实，但该关系对汇总期和用于计算交易量波动率的观察频率都不稳健。我们证明，他们的程序涉及到由于体积的持久性和偏性的估计偏差。当使用一种基于高频数据的替代方法来衡量流动性不确定性时，这种关系被证明是正的。然而，基于流动性不确定性的投资组合策略似乎并不有利可图。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

The Liquidity Uncertainty Premium Puzzle

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The Liquidity Uncertainty Premium Puzzle

The puzzling negative relation between liquidity uncertainty and asset returns, originally put forward by Chordia, Subrahmanyam, and Anshuman (2001) and confirmed by the subsequent empirical literature up to date, is neither robust to the aggregation period, nor to the observation frequency used to compute the volatility of trading volume. We demonstrate that their procedure involves an estimation bias due to the persistence and skewness of volumes. When using an alternative approach based on high-frequency data to measure liquidity uncertainty, the relationship turns out to be positive. However, portfolio strategies based on liquidity uncertainty do not appear to be profitable.

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来源期刊

Journal of Time Series Analysis 数学-数学跨学科应用

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.