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Analysis of causality and information flow between time series in different fields is an important research topic in the literature and different causality tests have been proposed. The most widely used of these are the Granger, Toda-Yamamoto, and Hatemi-J causality tests. Although these tests reveal whether there is causality between time series and the direction of this causality, they do not measure the degree of causality. Transfer entropy is a new information theory-based method and a non-parametric method that can be used to measure causality and information flow between time series, and it can detect asymmetric and nonlinear information flow between two time series. In this study, the causality and information flow between the BIST100 index and the exchange rate of dollar were analyzed using transfer entropy, and the sliding window method was used to reveal how the information flow changed over time. In order to demonstrate the robustness of the results, the results obtained from three different sized time windows are presented. [ABSTRACT FROM AUTHOR]

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