Granger Causality: A Review and Recent Advances

Shojaie, Ali; Fox, Emily B.

doi:10.1146/annurev-statistics-040120-010930

Cited by 147 publications

(77 citation statements)

References 122 publications

(149 reference statements)

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“… Granger (1969) raised a definition of "causality" in 1969, explaining Y t causes X t if including the past value of the time series Y t can improve the prediction of future X t . It is a statistically predictable relationship rather than real causality based on the logic of counterfactuals ( Shojaie & Fox, 2022 ). Therefore, scholars call this relationship as "Granger causality" to distinguish it from other notions of causality.…”

Section: Literature Reviewmentioning

confidence: 99%

Can people hear others’ crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China

Zhou

Miao

Jiang

et al. 2022

Information Processing & Management

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Section: Literature Reviewmentioning

confidence: 99%

Can people hear others’ crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China

Zhou

Miao

Jiang

et al. 2022

Information Processing & Management

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“…Statistically, however, this seems to be possible; Granger [ 35 ] devised a practical statistical mechanism [ 58 ] for measuring causality. Granger stated that some temporal ordering normally seen in time series data (e.g., the stock price) may infer some causal predictive ability [ 59 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…To Granger’s own surprise [ 64 ], specific terms were created for Granger’s view of causality, including ‘Granger causality’ [ 59 ], ‘Granger cause’ [ 53 ] and ‘G-cause(s)’ [ 65 ]. Any of these terms can be used if Granger’s condition for causality and the subsequent cause-effect is seen.…”

Section: Literature Reviewmentioning

confidence: 99%

The Causality and Uncertainty of the COVID-19 Pandemic to Bursa Malaysia Financial Services Index’s Constituents

Zuhud

Musa

Ismail

et al. 2022

Entropy

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Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index’s constituents comprise several of the strongest performing financial constituents in Bursa Malaysia’s Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent’s causal influence or intensity relative to each other’s performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent’s causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia’s sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index’s largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making.

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“…The linear Granger causality approach that is often considered in financial lead-lag studies [Shojaie and Fox, 2021] can be viewed as an extension of a functional linear cross-correlation-based approach that takes into account auto-correlation and also filters for statistical significance. General Granger causality methods may also use non-linear functional forms to capture the association between time series.…”

Section: Lead-lag Metrics Based On a Functional Of The Cross-correlationmentioning

confidence: 99%

Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

Bennett¹,

Cucuringu²,

Reinert³

2022

Preprint

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In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead-lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead-lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead-lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead-lag metric and directed network clustering components. Our framework is validated on both a synthetic generative model for multivariate lead-lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead-lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead-lag relations and demonstrate how these can be used for the construction of predictive financial signals.

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Granger Causality: A Review and Recent Advances

Cited by 147 publications

References 122 publications

Can people hear others’ crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China

Can people hear others’ crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China

The Causality and Uncertainty of the COVID-19 Pandemic to Bursa Malaysia Financial Services Index’s Constituents

Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

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