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A closer look at co-movements among stock returns

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  • Zebedee, Allan A.
  • Kasch-Haroutounian, Maria

Abstract

Correlation among financial assets is widely recognized; however, the mechanics of the relationship are not well understood. This paper investigates the microstructure of the co-movement of stock returns. The goal is to improve our understanding of correlation among stock returns by examining the conditions under which asset returns co-move on an intra-day basis. The methodology combines a traditional lead-lag model with a modified or pseudo-error correction model. Empirical evidence is presented to suggest the speed of adjustment between paired asset intra-day returns is a function of asymmetric information. Specifically, the wider an asset's spread, the faster the asset will converge to the intra-day returns of other similar assets. This result is consistent with partial adjustment model presented by Chan (Chan, K. (1993). Imperfect information and cross-autocorrelation among stock prices. The Journal of Finance:1211-1230.) which suggests market makers gain from monitoring other market makers in periods of uncertainty.

Suggested Citation

  • Zebedee, Allan A. & Kasch-Haroutounian, Maria, 2009. "A closer look at co-movements among stock returns," Journal of Economics and Business, Elsevier, vol. 61(4), pages 279-294, July.
    • Handle: RePEc:eee:jebusi:v:61:y::i:4:p:279-294
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    References listed on IDEAS

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    3. Sahar Albosaily & Serguei Pergamenchtchikov, 2021. "Optimal Investment and Consumption for Multidimensional Spread Financial Markets with Logarithmic Utility," Stats, MDPI, vol. 4(4), pages 1-15, November.
    4. Sahar Albosaily & Serguei Pergamenshchikov, 2018. "Optimal investment and consumption for Ornstein-Uhlenbeck spread financial markets with logarithmic utility," Papers 1809.08139, arXiv.org.
    5. João Frois Caldeira & Gulherme Valle Moura, 2013. "Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy," Brazilian Review of Finance, Brazilian Society of Finance, vol. 11(1), pages 49-80.
    6. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
    7. Arnab Chakrabarti & Rituparna Sen, 2023. "Copula Estimation for Nonsynchronous Financial Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 116-149, May.
    8. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Impact of the tick-size on financial returns and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4828-4843.
    9. Henryk Gurgul & Artur Machno, 2017. "The impact of asynchronous trading on Epps effect on Warsaw Stock Exchange," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 287-301, June.

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