IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1302.3197.html
   My bibliography  Save this paper

Bridging stylized facts in finance and data non-stationarities

Author

Listed:
  • Sabrina Camargo
  • Silvio M. Duarte Queiros
  • Celia Anteneodo

Abstract

Employing a recent technique which allows the representation of nonstationary data by means of a juxtaposition of locally stationary patches of different length, we introduce a comprehensive analysis of the key observables in a financial market: the trading volume and the price fluctuations. From the segmentation procedure we are able to introduce a quantitative description of a group of statistical features (stylizes facts) of the trading volume and price fluctuations, namely the tails of each distribution, the U-shaped profile of the volume in a trading session and the evolution of the trading volume autocorrelation function. The segmentation of the trading volume series provides evidence of slow evolution of the fluctuating parameters of each patch, pointing to the mixing scenario. Assuming that long-term features are the outcome of a statistical mixture of simple local forms, we test and compare different probability density functions to provide the long-term distribution of the trading volume, concluding that the log-normal gives the best agreement with the empirical distribution. Moreover, the segmentation of the magnitude price fluctuations are quite different from the results for the trading volume, indicating that changes in the statistics of price fluctuations occur at a faster scale than in the case of trading volume.

Suggested Citation

  • Sabrina Camargo & Silvio M. Duarte Queiros & Celia Anteneodo, 2013. "Bridging stylized facts in finance and data non-stationarities," Papers 1302.3197, arXiv.org, revised May 2013.
    • Handle: RePEc:arx:papers:1302.3197
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1302.3197
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    2. Armand Joulin & Augustin Lefevre & Daniel Grunberg & Jean-Philippe Bouchaud, 2008. "Stock price jumps: news and volume play a minor role," Papers 0803.1769, arXiv.org.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," NBER Technical Working Papers 0279, National Bureau of Economic Research, Inc.
    4. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    5. Matthieu Wyart & Jean-Philippe Bouchaud & Julien Kockelkoren & Marc Potters & Michele Vettorazzo, 2006. "Relation between Bid-Ask Spread, Impact and Volatility in Double Auction Markets," Papers physics/0603084, arXiv.org, revised Mar 2007.
    6. Silvio M. Duarte Queiros & Celia Anteneodo & Constantino Tsallis, 2005. "Power-law distributions in economics: a nonextensive statistical approach," Papers physics/0503024, arXiv.org.
    7. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    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. Edina Berlinger & Barbara Dömötör & Ferenc Illés & Kata Váradi, 2016. "Stress Indicator for Clearing Houses," Central European Business Review, Prague University of Economics and Business, vol. 2016(4), pages 47-60.
    2. Ponta, Linda & Trinh, Mailan & Raberto, Marco & Scalas, Enrico & Cincotti, Silvano, 2019. "Modeling non-stationarities in high-frequency financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 173-196.

    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. Seemann, Lars & McCauley, Joseph L. & Gunaratne, Gemunu H., 2011. "Intraday volatility and scaling in high frequency foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 121-126, June.
    2. Torben G. ANDERSEN & Tim BOLLERSLEV & Nour MEDDAHI, 2002. "Correcting The Errors : A Note On Volatility Forecast Evaluation Based On High-Frequency Data And Realized Volatilities," Cahiers de recherche 21-2002, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Gilles Zumbach, 2007. "Time reversal invariance in finance," Papers 0708.4022, arXiv.org.
    4. B. Tóth & Z. Eisler & F. Lillo & J. Kockelkoren & J.-P. Bouchaud & J.D. Farmer, 2012. "How does the market react to your order flow?," Quantitative Finance, Taylor & Francis Journals, vol. 12(7), pages 1015-1024, May.
    5. M. Bartolozzi, 2010. "A multi agent model for the limit order book dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 78(2), pages 265-273, November.
    6. Lima, Leonardo S. & Santos, Greicy K.C., 2018. "Stochastic process with multiplicative structure for the dynamic behavior of the financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 222-229.
    7. M. Cristelli & V. Alfi & L. Pietronero & A. Zaccaria, 2010. "Liquidity crisis, granularity of the order book and price fluctuations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 41-49, January.
    8. Ole E. Barndorff-Nielsen & Bent Nielsen & Neil Shephard & Carla Ysusi, 2002. "Measuring and forecasting financial variability using realised variance with and without a model," Economics Papers 2002-W21, Economics Group, Nuffield College, University of Oxford.
    9. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    10. Geoff Willis, 2011. "Pricing, liquidity and the control of dynamic systems in finance and economics," Papers 1105.5503, arXiv.org.
    11. Willis, Geoff, 2011. "Why money trickles up – wealth & income distributions," MPRA Paper 30851, University Library of Munich, Germany.
    12. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics," Economics Papers 2002-W13, Economics Group, Nuffield College, University of Oxford, revised 18 Mar 2002.
    13. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    14. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    15. S. M.D. Queirós, 2008. "On discrete stochastic processes with long-lasting time dependence in the variance," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 66(1), pages 137-148, November.
    16. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    17. Giuseppe Brandi & T. Di Matteo, 2020. "On the statistics of scaling exponents and the Multiscaling Value at Risk," Papers 2002.04164, arXiv.org, revised Mar 2021.
    18. Gontis, V. & Ruseckas, J. & Kononovičius, A., 2010. "A long-range memory stochastic model of the return in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 100-106.
    19. Sabiou Inoua, 2015. "The Intrinsic Instability of Financial Markets," Papers 1508.02203, arXiv.org.
    20. Bertram, William K., 2005. "A threshold model for Australian Stock Exchange equities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 561-576.

    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:arx:papers:1302.3197. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.