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Time-varying pattern causality inference in global stock markets
International Review of Financial Analysis ( IF 7.5 ) Pub Date : 2021-06-12 , DOI: 10.1016/j.irfa.2021.101806
Tao Wu , Xiangyun Gao , Sufang An , Siyao Liu

Causality analysis can reveal the intrinsic interactions in financial markets. Though Granger causality test and transfer entropy method have successfully determined positive and negative causal interactions, they fail to reveal a more complex causal interaction, dark causality. Moreover, the causal relationship between variables may be time-varying. Thus, in this work, we are dedicated to determining the nature of causal interaction and explore the time-varying causality in global stock markets. To achieve this goal, pattern causality (PC) theory, cross-convergent mapping (CCM) theory, the sliding window method and complex networks are applied. By them, three causal interactions with different strength are revealed in global stock markets, and the causal strength is time-varying in different periods both in simulated systems and financial markets. While the dominant causal interaction is stable except for some stock pairs in frontier and emerging markets. In total, we determine the positive dominant causality in global stock markets; that is, the overall consistent trend among stocks can be explored. Additionally, we discover some exceptions that show negative dominant causality, where the reverse trend can be revealed among them; moreover, their dominant causality is time-varying. These uncertainties should receive great attention from investors and government managers.



中文翻译:

全球股票市场的时变模式因果推断

因果关系分析可以揭示金融市场中的内在相互作用。尽管格兰杰因果检验和转移熵方法已经成功地确定了正负因果相互作用,但它们未能揭示更复杂的因果相互作用,即暗因果关系。此外,变量之间的因果关系可能是随时间变化的。因此,在这项工作中,我们致力于确定因果相互作用的性质并探索全球股票市场中随时间变化的因果关系。为了实现这一目标,应用了模式因果关系 (PC) 理论、交叉收敛映射 (CCM) 理论、滑动窗口方法和复杂网络。通过它们,揭示了全球股票市场中三个不同强度的因果相互作用,并且在模拟系统和金融市场中,因果强度在不同时期是随时间变化的。除了前沿市场和新兴市场的一些股票对外,主要的因果关系是稳定的。总的来说,我们确定了全球股市的正主导因果关系;也就是说,可以探索股票之间的整体一致趋势。此外,我们发现了一些显示负主导因果关系的例外,其中可以揭示反向趋势;此外,它们的主要因果关系是随时间变化的。这些不确定性应该引起投资者和政府管理者的高度重视。我们发现了一些显示负显性因果关系的例外,其中可以揭示相反的趋势;此外,它们的主要因果关系是随时间变化的。这些不确定性应该引起投资者和政府管理者的高度重视。我们发现了一些显示负显性因果关系的例外,其中可以揭示相反的趋势;此外,它们的主要因果关系是随时间变化的。这些不确定性应该引起投资者和政府管理者的高度重视。

更新日期:2021-06-25
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