Information Flow Analysis between EPU and Other Financial Time Series.

Can-Zhong Yao

doi:10.3390/e22060683

Abstract

We investigate the strength and direction of information flow among economic policy uncertainty (EPU), US imports and exports to China, and the CNY/US exchange rate by using the novel concept of effective transfer entropy (ETE) with a sliding window methodology. We verify that this new method can capture dynamic orders effectively by validating them with the linear transfer entropy (TE) and Granger causality methods. Analysis shows that since 2016, US economic policy has contributed substantially to China-US bilateral trade and that China is making passive adjustments based on this trade volume. Unlike trade market conditions, China’s economic policy has significantly influenced the exchange rate fluctuation since 2016, which has, in turn, affected US economic policy.

Highlights

Causality is a central concept in natural sciences and is commonly understood to describe a situation in which a process evolving in time has some observable effect on a second process
Transfer entropy arises from the formulation of conditional mutual information; when conditioning on the past values of variables, it quantifies the reduction in uncertainty provided by these past values when predicting the dependent variable, which presents a natural way to model the statistical causality between variables in multivariate distributions
Since traditional linear methods cannot identify the dynamic orders between time series and unable to track specific time points when structural fluctuations occur, we apply the improved transfer entropy to explore the information flow between all sequences

Summary

Introduction

Causality is a central concept in natural sciences and is commonly understood to describe a situation in which a process evolving in time has some observable effect on a second process. There is a long history of determining whether some change truly causes another [1,2], especially if the effect is not deterministic and is observed only in aggregate. The Granger causality test, which was first proposed in 1969 [3], is a statistical hypothesis test for determining whether one time series is useful for forecasting another. The so-called Granger causality describes the extent to which a response in the dependent series can be explained by a change in the first. Transfer entropy arises from the formulation of conditional mutual information; when conditioning on the past values of variables, it quantifies the reduction in uncertainty provided by these past values when predicting the dependent variable, which presents a natural way to model the statistical causality between variables in multivariate distributions. In the general formulation, transfer entropy is a model-free statistic that can measure the time-directed transfer of information between stochastic variables and provides an asymmetric method to measure information transfer

Methods

Results

Conclusion