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Published March 12, 2018 | Published
Journal Article Open

A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates

Abstract

In this study, we assess the dynamic evolution of short-term correlation, long-term cointegration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based long-run Granger causality vary significantly over the whole sample period. The degree of dynamic correlation and cointegration between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of economic, financial and political shocks. Meanwhile, we observe the weaker and decreasing correlation and cointegration among the three developed stock markets during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 2007–09 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic integration and causality between all pairs of stock indices, with that influence increasing under the local currency terms. Our results suggest that the potential for diversifying risk by investing in the US, UK and Eurozone stock markets is limited during the periods of economic, financial and political shocks.

Additional Information

© 2018 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: July 4, 2017; Accepted: February 23, 2018; Published: March 12, 2018. Data Availability: Data are available from the Thomson Reuters Datastream (see also p. 4 of the paper). The data range from January 1st, 1980 to December 29th, 2015, apart from that for the EURO STOXX 50 index, for which data was available from February 26th, 1998. The samples of the S&P 500 and FTSE 100 consist of 1879 observations EURO STOXX 50 was launched on February 26th, 1998 each, and that of the EURO STOXX 50 index contains 932 observations. Access has been granted to the Thomson Reuters DataStream by the University of Liverpool account. We can confirm that anyone who has access to Thomson Reuters DataStream can download the same datasets. To have access to that particular Database, the University or individual needs to purchase special licences, found here: https://financial.thomsonreuters.com/en/products/tools-applications/trading-investment-tools/datastream-macroeconomic-analysis.html. This work was funded by the Engineering and Physical Sciences Research Council (Grant No. EP/L015927/1 to AP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. The authors would like to acknowledge the support for this work provided by the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments (EP/L015927/1). We would like to thank the Associate Editors, Ryan Whitby and Wei-Xing Zhou, and the two anonymous reviewers as well as the participants at the 60th ISI World Statistics Congress, the 2nd Quantitative Finance and Risk Analysis (QFRA2016) symposium, and at the seminar talks in the University of Liverpool (UK), Shanghai University and Chinese Academy of Sciences (China) and Monash University (Australia), for their helpful comments. Particular thanks to Ai Han (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China) who read very carefully the last version of our paper and the comments she made. Any remaining errors are ours. Author Contributions: Conceptualization: Rosario N. Mantegna, Athanasios A. Pantelous. Data curation: Yanhua Chen. Formal analysis: Yanhua Chen, Athanasios A. Pantelous. Funding acquisition: Athanasios A. Pantelous. Investigation: Yanhua Chen, Rosario N. Mantegna, Athanasios A. Pantelous, Konstantin M. Zuev. Methodology: Yanhua Chen, Athanasios A. Pantelous, Konstantin M. Zuev. Project administration: Rosario N. Mantegna, Athanasios A. Pantelous, Konstantin M. Zuev. Resources: Yanhua Chen, Athanasios A. Pantelous. Software: Yanhua Chen. Supervision: Rosario N. Mantegna, Athanasios A. Pantelous, Konstantin M. Zuev. Validation: Yanhua Chen, Athanasios A. Pantelous, Konstantin M. Zuev. Visualization: Yanhua Chen, Athanasios A. Pantelous, Konstantin M. Zuev. Writing ± original draft: Yanhua Chen, Athanasios A. Pantelous. Writing ± review & editing: Yanhua Chen, Rosario N. Mantegna, Athanasios A. Pantelous, Konstantin M. Zuev.

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