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Measuring the effects of geographical distance on stock market correlation

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  • Eckel, Stefanie
  • Löffler, Gunter
  • Maurer, Alina
  • Schmidt, Volker

Abstract

Recent studies suggest that the correlation of stock returns increases with decreasing geographical distance. However, there is some debate on the appropriate methodology for measuring the effects of distance on correlation. We modify a regression approach suggested in the literature and complement it with an approach from spatial statistics, the mark correlation function. For the stocks contained in the S&P 500 that we examine, both approaches lead to similar results. Contrary to previous studies we find that beyond 50 miles geographical proximity is irrelevant for stock return correlations. For distances below 50 miles, we can show that the magnitude of local correlations varies with investor sentiment.

Suggested Citation

  • Eckel, Stefanie & Löffler, Gunter & Maurer, Alina & Schmidt, Volker, 2011. "Measuring the effects of geographical distance on stock market correlation," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 237-247, March.
  • Handle: RePEc:eee:empfin:v:18:y:2011:i:2:p:237-247
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    References listed on IDEAS

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    Cited by:

    1. Fu, Shihe & Shan, Liwei, 2011. "Agglomeration Economies and Local Comovement of Stock Returns," MPRA Paper 31887, University Library of Munich, Germany.
    2. He, Hongbo & Chen, Shou & Yao, Shujie & Ou, Jinghua, 2014. "Financial liberalisation and international market interdependence: Evidence from China’s stock market in the post-WTO accession period," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 434-444.
    3. Rothonis, Stephanie & Tran, Duy & Wu, Eliza, 2016. "Does national culture affect the intensity of volatility linkages in international equity markets?," Research in International Business and Finance, Elsevier, pages 85-95.
    4. Tam, Pui Sun, 2014. "A spatial–temporal analysis of East Asian equity market linkages," Journal of Comparative Economics, Elsevier, pages 304-327.
    5. Matthias Arnold & Sebastian Stahlberg & Dominik Wied, 2013. "Modeling different kinds of spatial dependence in stock returns," Empirical Economics, Springer, pages 761-774.

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