Non-linear Granger causality in the currency futures returns

Asimakopoulos, Ioannis; Ayling, David; Mahmood, Wan Mansor Wan

doi:10.1016/s0165-1765(00)00219-6

Cited by 39 publications

(25 citation statements)

References 8 publications

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“…However, temporal correlation or dependency does not necessarily indicate causality. Granger causality test [10] was first introduced in the area of econometrics for time series analysis and has since gained tremendous success across many domains due to its simplicity and robustness [12,13,3,8].…”

Section: Related Workmentioning

confidence: 99%

Discovery of Causal Time Intervals

Li¹,

Zheng²,

Agarwal³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Causality analysis, beyond "mere" correlations, has become increasingly important for scientific discoveries and policy decisions. Many of these real-world applications involve time series data. A key observation is that the causality between time series could vary significantly over time. For example, a rain could cause severe traffic jams during the rush hours, but has little impact on the traffic at midnight. However, previous studies mostly look at the whole time series when determining the causal relationship between them. Instead, we propose to detect the partial time intervals with causality. As it is time consuming to enumerate all time intervals and test causality for each interval, we further propose an efficient algorithm that can avoid unnecessary computations based on the bounds of F -test in the Granger causality test. We use both synthetic datasets and real datasets to demonstrate the efficiency of our pruning techniques and that our method can effectively discover interesting causal intervals in the time series data.

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Section: Related Workmentioning

confidence: 99%

Discovery of Causal Time Intervals

Li¹,

Zheng²,

Agarwal³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…According to Asimakopoulos et al [22], the causal relationships can be driven by a common informational factor which is assumed to be the persistence in variance. To make sure our results more robust, we implement a GARCH (1, 1) model filtering on the return to capture the common factor, and the corresponding residuals are explored pairwise by the unconditional and conditional…”

Section: Robustness Testsmentioning

confidence: 99%

The Impact of US Stock Market on the Co-Movements of BRIC Stock Markets—Evidence from Linear Conditional Granger Causality

Wang¹,

Yang²,

Ma³

2017

OJS

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This paper investigates the impact of the US stock market on the co-movements among the BRIC stock markets using conditional Granger causality which allows a comprehensive exploration on direct and indirect causality. The results from linear conditional causality test show a strong influence of the US stock market on the co-movements of BRIC. Our findings identify the US stock market which is the main inner factor making major contributions to the co-movements among the BRIC stock markets. Further, this study provides robust evidence that the co-movements cannot be significantly influenced by the common information factor. These findings show a more complete picture of the relationships between the US and the BRIC stock markets, offering important implications for policymakers and investors.

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“…This problem is raised in contributions which are concerned with nonlinear causality tests (see e.g. Abhyankar [1] and Asimakopoulos [2]). The starting point for further investigations was the nonparametric statistical method for uncovering nonlinear causal effects presented by Baek and Brock [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to minimize the mean square error the linear combination of r 2 ZOt and RV t NW was taken into account. The optimal, conditionally unbiased, minimum variance estimator of realized volatility in the class of linear estimators is: The parameter gives a relative importance factor that indirectly stands for a portion of the volatility observed during the session.…”

Section: Realized Volatilitymentioning

confidence: 99%