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Stock Market Integration: Granger Causality Testing with Respect to Nonsynchronous Trading Effects

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Abstract

In this paper, we perform Granger causality analysis on stock market indices from several Asian, European, and U.S. markets. Using daily data, we point out the potential problems caused by the presence of nonsynchronous trading effects. We deal with two kinds of nonsynchronicity – one induced by differing numbers of observations in the series being analyzed and the other related to the different time zones in which the markets operate. To address the first problem, we propose a data-matching process. To address the second problem, we modify the regressions used in the Granger causality testing. When comparing the empirical results obtained using the standard technique and our modified methodology, we find substantially different results. Most of the relationships that are subject to nonsynchronous trading are not significant in the general case. However, when we use the adjusted methodology, the null hypothesis of a Granger non-causal relationship is rejected in all cases.

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Bibliographic Info

Article provided by Charles University Prague, Faculty of Social Sciences in its journal Finance a uver - Czech Journal of Economics and Finance.

Volume (Year): 60 (2010)
Issue (Month): 5 (December)
Pages: 414-425

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Handle: RePEc:fau:fauart:v:60:y:2010:i:5:p:414-425

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Keywords: stock market integration; nonsynchronous trading; Granger causality;

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Cited by:
  1. Joanna Olbrys, 2013. "Price and Volatility Spillovers in the Case of Stock Markets Located in Different Time Zones," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 49(S2), pages 145-157, March.
  2. Ceylan Onay & Gözde Ünal, 2012. "Cointegration and Extreme Value Analyses of Bovespa and the Istanbul Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(1), pages 66-90, February.
  3. Korhonen, Iikka & Peresetsky , Anatoly, 2013. "What determines stock market behavior in Russia and other emerging countries?," BOFIT Discussion Papers 4/2013, Bank of Finland, Institute for Economies in Transition.
  4. Baumöhl, Eduard & Lyócsa, Štefan, 2012. "Constructing weekly returns based on daily stock market data: A puzzle for empirical research?," MPRA Paper 43431, University Library of Munich, Germany.
  5. Baumöhl, Eduard, 2013. "Stock market integration between the CEE-4 and the G7 markets: Asymmetric DCC and smooth transition approach," MPRA Paper 43834, University Library of Munich, Germany.
  6. Silvo Dajcman, 2012. "The Dynamics of Return Comovement and Spillovers Between the Czech and European Stock Markets in the Period 1997–2010," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(4), pages 368-390, August.
  7. Baumöhl, Eduard & Lyócsa, Štefan, 2014. "Volatility and dynamic conditional correlations of worldwide emerging and frontier markets," Economic Modelling, Elsevier, vol. 38(C), pages 175-183.
  8. Baumohl, Eduard & Lyocsa, Stefan, 2013. "Volatility and dynamic conditional correlations of European emerging stock markets," MPRA Paper 49898, University Library of Munich, Germany.

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