IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-00621244.html
   My bibliography  Save this paper

High frequency correlation modelling

Author

Listed:
  • Nicolas Huth

    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris, FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

  • Frédéric Abergel

    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris, FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

Abstract

Many statistical arbitrage strategies, such as pair trading or basket trading, are based on several assets. Optimal execution routines should also take into account correlation between stocks when proceeding clients orders. However, not so much effort has been devoted to correlation modelling and only few empirical results are known about high frequency correlation. We develop a theoretical framework based on correlated point processes in order to capture the Epps effect in section 1. We show in section 2 that this model converges to correlated Brownian motions when moving to large time scales. A way of introducing non-Gaussian correlations is also discussed in section 2. We conclude by addressing the limits of this model and further research on high frequency correlation.

Suggested Citation

  • Nicolas Huth & Frédéric Abergel, 2010. "High frequency correlation modelling," Post-Print hal-00621244, HAL.
  • Handle: RePEc:hal:journl:hal-00621244
    Note: View the original document on HAL open archive server: https://hal.science/hal-00621244
    as

    Download full text from publisher

    File URL: https://hal.science/hal-00621244/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tóth, Bence & Kertész, János, 2006. "Increasing market efficiency: Evolution of cross-correlations of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 505-515.
    2. Ovidiu V. Precup & Giulia Iori, 2007. "Cross-correlation Measures in the High-frequency Domain," The European Journal of Finance, Taylor & Francis Journals, vol. 13(4), pages 319-331.
    3. Bence Toth & Janos Kertesz, 2009. "The Epps effect revisited," Quantitative Finance, Taylor & Francis Journals, vol. 9(7), pages 793-802.
    4. Tóth, Bence & Kertész, János, 2007. "On the origin of the Epps effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 54-58.
    5. Roberto Renò, 2003. "A Closer Look At The Epps Effect," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 87-102.
    6. Bence Toth & Janos Kertesz, 2007. "On the origin of the Epps effect," Papers physics/0701110, arXiv.org, revised Feb 2007.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicolas Huth & Frédéric Abergel, 2012. "The times change: multivariate subordination, empirical facts," Post-Print hal-00620841, HAL.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nicolas Huth & Frédéric Abergel, 2012. "The times change: multivariate subordination, empirical facts," Post-Print hal-00620841, HAL.
    3. Materassi, Donatello & Innocenti, Giacomo, 2009. "Unveiling the connectivity structure of financial networks via high-frequency analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3866-3878.
    4. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
    5. Iacopo Mastromatteo & Matteo Marsili & Patrick Zoi, 2010. "Financial correlations at ultra-high frequency: theoretical models and empirical estimation," Papers 1011.1011, arXiv.org, revised Feb 2011.
    6. 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.
    7. Drakos, Anastassios A., 2016. "Does the relationship between small and large portfolios’ returns confirm the lead–lag effect? Evidence from the Athens Stock Exchange," Research in International Business and Finance, Elsevier, vol. 36(C), pages 546-561.
    8. Takaki Hayashi & Yuta Koike, 2017. "No arbitrage and lead-lag relationships," Papers 1712.09854, arXiv.org.
    9. Arnab Chakrabarti & Rituparna Sen, 2019. "Copula estimation for nonsynchronous financial data," Papers 1904.10182, arXiv.org, revised Sep 2020.
    10. 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.
    11. Wang, Yudong & Liu, Li & Gu, Rongbao, 2009. "Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 271-276, December.
    12. Patrick Chang & Roger Bukuru & Tim Gebbie, 2019. "Revisiting the Epps effect using volume time averaging: An exercise in R," Papers 1912.02416, arXiv.org, revised Feb 2020.
    13. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    14. Gabriele La Spada & J. Doyne Farmer & Fabrizio Lillo, 2010. "Tick size and price diffusion," Papers 1009.2329, arXiv.org, revised Oct 2010.
    15. 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.
    16. Xiufeng Yan & Qi Tang, 2021. "Network analysis regarding international trade network," Papers 2111.02633, arXiv.org.
    17. Hayashi, Katsuhiko & Kaizoji, Taisei & Pichl, Lukáš, 2007. "Correlation patterns of NIKKEI index constituents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 16-21.
    18. Andreea B. Dragut, 2012. "Stock Data Clustering and Multiscale Trend Detection," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 87-105, March.
    19. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    20. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-00621244. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.