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Time scales involved in emergent market coherence

Author

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  • Kwapień, J
  • Drożdż, S
  • Speth, J

Abstract

In addressing the question of the time scales characteristic for the market formation, we analyze high-frequency tick-by-tick data from the NYSE and from the German market. By using returns on various time scales ranging from seconds or minutes up to 2 days, we compare magnitude of the largest eigenvalue of the correlation matrix for the same set of securities but for different time scales. For various sets of stocks of different capitalization (and the average trading frequency), we observe a significant elevation of the largest eigenvalue with increasing time scale. Our results from the correlation matrix study can be considered as a manifestation of the so-called Epps effect. There is no unique explanation of this effect and it seems that many different factors play a role here. One of such factors is randomness in transaction moments for different stocks. Another interesting conclusion to be drawn from our results is that in the contemporary markets the emergence of significant correlations occurs on time scales much smaller than in the more distant history.

Suggested Citation

  • Kwapień, J & Drożdż, S & Speth, J, 2004. "Time scales involved in emergent market coherence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 231-242.
    • Handle: RePEc:eee:phsmap:v:337:y:2004:i:1:p:231-242
    DOI: 10.1016/j.physa.2004.01.050
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    Citations

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    Cited by:

    1. S. Valeyre & D. S. Grebenkov & S. Aboura, 2018. "Emergence of correlations between securities at short time scales," Papers 1807.05015, arXiv.org.
    2. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Pawe{l} O'swic{e}cimka & Marek Stanuszek, 2018. "Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017," Papers 1812.08548, arXiv.org, revised Jun 2019.
    3. Lee, Sangwook & Kim, Min Jae & Kim, Soo Yong, 2011. "Interest rates factor model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2531-2548.
    4. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2021. "Financial Return Distributions: Past, Present, and COVID-19," Papers 2107.06659, arXiv.org.
    5. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Marcin Wk{a}torek, 2023. "What is mature and what is still emerging in the cryptocurrency market?," Papers 2305.05751, arXiv.org.
    6. Sebastien Valeyre & Denis S Grebenkov & Sofiane Aboura, 2019. "Emergence of correlations between securities at short time scales," Post-Print hal-02343888, HAL.
    7. Jaros{l}aw Kwapie'n & Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z, 2021. "Cryptocurrency Market Consolidation in 2020--2021," Papers 2112.06552, arXiv.org.
    8. Sharkasi, Adel & Crane, Martin & Ruskin, Heather J. & Matos, Jose A., 2006. "The reaction of stock markets to crashes and events: A comparison study between emerging and mature markets using wavelet transforms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(2), pages 511-521.
    9. Frank Emmert-Streib & Matthias Dehmer, 2010. "Influence of the Time Scale on the Construction of Financial Networks," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
    10. 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.
    11. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    12. Goswami, B. & Ambika, G. & Marwan, N. & Kurths, J., 2012. "On interrelations of recurrences and connectivity trends between stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4364-4376.

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