CAUSALITY, INFORMATION FLOW, AND CO-MOVEMENT ANALYSIS OF MAJOR STOCK INDICES

Cengiz Karatas () and Gazanfer Unal ()
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Cengiz Karatas: Financial Economics Graduate Program, Yeditepe University, Istanbul 34755, Turkey
Gazanfer Unal: Faculty of Economics, Administrative and Social Sciences, Bahçeşehir University, Istanbul, Turkey

Annals of Financial Economics (AFE), 2022, vol. 17, issue 03, 1-21

Abstract: Stock indices are key indicators of the economy since they indicate the strength of a country’s stock market. For this reason, causality, information flow and co-movement analysis of stock indices gain importance in comparing countries’ economies. Here, we apply a novel approach by analyzing the results of two different methodologies; in wavelet coherence (WTC) analysis, the co-movement between stock indices provided and coherent areas can be shown, and information flow is indicated for five-year periods, especially on coherent zones by Transfer Entropy (TE), which detects cause-and-effect relations. This paper analyzed the information flow and co-movement among FTSE100 in the United Kingdom, the DAX in Germany and S&P500 Index in the United States stock indices. Three different results are obtained as follows: (1) DAX is on the leading side in general for five-year periods, (2) bidirectional information flows arise for every pair in the coherent periods and (3) TE-guided WTC analysis shows that TE sign change can be explained by phase angle direction obtained with WTC. These results indicate that both the methods yield proper outcomes in coherent time zones and during financial crisis like the COVID period, which we have faced for two years; for this reason, the results were also obtained for the COVID period, and in general, that shows DAX dominated other indices. We published this study to help researchers understand the connectedness between stock indices and investors avoiding risk in their stock portfolios, especially during financial crisis periods.

Keywords: Co-movement; wavelet transform; transfer entropy; information flow; causality; stock (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S2010495222500191

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