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Stock exchange mergers: a dynamic correlation analysis on Euronext

Abstract

This article investigates the role of Stock Exchange Mergers on stock market return co-movements. Using a dynamic conditional correlation model proposed by Engle (J Bus Econ Stat 20:339–350, 2002), the Euronext Stock Exchange was analyzed, and findings point to an increase in correlation levels of stock return among Euronext unitholders. In short, Euronext stock exchange mergers increased interdependency among these markets, which means that the possibility of diversifying investment risk in these markets is reduced.

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Fig. 1

Notes

  1. 1.

    Portfolio theory, showing how investors can create portfolios of individual investments to optimally trade off risk versus return, was developed by Harry Markowitz (1952, 1959) and Roy (1952). According to the authors and several others that later followed their steps (Pretorius 2002, among others), if the correlation between stocks increases then the diversification possibilities decrease.

  2. 2.

    Previous studies have used the DCC model to analyze the virtual integration of financial markets as the Mercado Integrado Latino Americano, MILA, (Espinosa et al. 2017; Mellado and Escobari 2015). On the other hand, an extension of the DCC model called Flexible Dynamic Conditional Correlation (Billio et al. 2006) has been used to analyze the conditional correlation over time in stock exchange mergers, more precisely the OMX integration (Hellström et al. 2013). In both cases, the type of integration differs from that of a Stock Exchange Mergers such as Euronext.

  3. 3.

    https://ipfs.io/ipfs/QmXoypizjW3WknFiJnKLwHCnL72vedxjQkDDP1mXWo6uco/wiki/Euronext.html

  4. 4.

    The DCC has three advantages over other estimation methods (Chiang et al. 2007). First, the DCC-GARCH model accounts for heteroscedasticity directly by estimating the correlation coefficients of the standardized residual. Secondly, exogenous controls may be included in the mean equation to account for common factors which affect the dynamics of Euronext. Finally, compared to alternative methods that model time-varying correlations, the DCC-GARCH is relatively parsimonious (Mellado and Escobari 2015). The results that we report are qualitatively the same when using the DCC-ARCH. We focus on explaining only one of the specifications.

  5. 5.

    Results from Zivot and Andrews test: AEX = −25.47; BFX = −27.82; CAC = −27.87; PSI = −26.39, all statistically significant at 1%.

  6. 6.

    This is a common way of investigating certain effects on conditional correlation (see Kim et al. (2006), Manolis and Kouretas (2011), Mellado and García (2014), Mellado and Escobari (2015), Espinosa et al. (2017), among others).

  7. 7.

    When we use Egrowth in the estimation of the models our observations are reduced, since we have data from 12/31/1996 to 12/31/2014 for this variable.

  8. 8.

    We estimate different models considering the annual frequency of the variables: CapMark / Gdp, Egrowth and Wgrowth. Additionally, we transformed these variables to daily frequency and we re-estimated the models (Table 6, Panels C and D). No differences were reported in the results.

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Acknowledgements

We are grateful to two anonymous reviewers and to Paulo Rodrigues, Co-Editor, for providing very helpful comments.

Funding

Dirección de Investigación, Científica y Tecnológica, Dicyt. Universidad de Santiago de Chile, Usach. (Proyecto Código 031563EC).

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Correspondence to Christian Espinosa-Méndez.

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Cite this article

Espinosa-Méndez, C., Gorigoitía, J. & Vieito, J. Stock exchange mergers: a dynamic correlation analysis on Euronext. Port Econ J (2019). https://doi.org/10.1007/s10258-019-00160-5

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Keywords

  • Stock exchange merger
  • Euronext
  • Dynamic conditional correlation

JEL classification

  • C12
  • C13
  • C58
  • G15