IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i4p1489-1496.html
   My bibliography  Save this article

A top–bottom price approach to understanding financial fluctuations

Author

Listed:
  • Rivera-Castro, Miguel A.
  • Miranda, José G.V.
  • Borges, Ernesto P.
  • Cajueiro, Daniel O.
  • Andrade, Roberto F.S.

Abstract

The presence of sequences of top and bottom (TB) events in financial series is investigated for the purpose of characterizing such switching points. They clearly mark a change in the trend of rising or falling prices of assets to the opposite tendency, are of crucial importance for the players’ decision and also for the market stability. Previous attempts to characterize switching points have been based on the behavior of the volatility and on the definition of microtrends. The approach used herein is based on the smoothing of the original data with a Gaussian kernel. The events are identified by the magnitude of the difference of the extreme prices, by the time lag between the corresponding events (waiting time), and by the time interval between events with a minimal magnitude (return time). Results from the analysis of the inter day Dow Jones Industrial Average index (DJIA) from 1928 to 2011 are discussed. q-Gaussian functions with power law tails are found to provide a very accurate description of a class of measures obtained from the series statistics.

Suggested Citation

  • Rivera-Castro, Miguel A. & Miranda, José G.V. & Borges, Ernesto P. & Cajueiro, Daniel O. & Andrade, Roberto F.S., 2012. "A top–bottom price approach to understanding financial fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1489-1496.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1489-1496
    DOI: 10.1016/j.physa.2011.11.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111008570
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2011.11.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Borges, Ernesto P, 2004. "Empirical nonextensive laws for the county distribution of total personal income and gross domestic product," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 255-266.
    2. Martinez, Alexandre Souto & González, Rodrigo Silva & Terçariol, César Augusto Sangaletti, 2008. "Continuous growth models in terms of generalized logarithm and exponential functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(23), pages 5679-5687.
    3. Destefano, Natália & Martinez, Alexandre Souto, 2011. "The additive property of the inconsistency degree in intertemporal decision making through the generalization of psychophysical laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(10), pages 1763-1772.
    4. Ernesto P. Borges, 2002. "Empirical nonextensive laws for the county distribution of total personal income and gross domestic product," Papers cond-mat/0205520, arXiv.org, revised Jan 2004.
    5. Lisa Borland, 2002. "Option Pricing Formulas based on a non-Gaussian Stock Price Model," Papers cond-mat/0204331, arXiv.org, revised Sep 2002.
    6. Cajueiro, Daniel O., 2006. "A note on the relevance of the q-exponential function in the context of intertemporal choices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 385-388.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    9. C. Anteneodo & C. Tsallis & A. S. Martinez, 2001. "Risk aversion in economic transactions," Papers cond-mat/0109203, arXiv.org, revised Jun 2002.
    10. Sornette, Didier & Johansen, Anders, 1997. "Large financial crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 411-422.
    11. Alexandre Souto Martinez & Rodrigo Silva Gonzalez & Cesar Augusto Sangaletti Tercariol, 2008. "Continuous growth models in terms of generalized logarithm and exponential functions," Papers 0803.2635, arXiv.org, revised May 2008.
    12. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    13. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for time-varying long-range dependence in volatility for emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 577-588.
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    15. repec:bla:jfinan:v:55:y:2000:i:4:p:1705-1770 is not listed on IDEAS
    16. Natalia Destefano & Alexandre Souto Martinez, 2010. "The additive property of the inconsistency degree in intertemporal decision making through the generalization of psychophysical laws," Papers 1010.5648, arXiv.org, revised May 2011.
    17. Takahashi, Taiki & Oono, Hidemi & Radford, Mark H.B., 2007. "Empirical estimation of consistency parameter in intertemporal choice based on Tsallis’ statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 338-342.
    18. Gene Savin & Paul Weller & Jānis Zvingelis, 0. "The Predictive Power of 'Head-and-Shoulders' Price Patterns in the U.S. Stock Market," Journal of Financial Econometrics, Oxford University Press, vol. 5(2), pages 243-265.
    19. Walid Omrane & Hervé Oppens, 2006. "The performance analysis of chart patterns: Monte Carlo simulation and evidence from the euro/dollar foreign exchange market," Empirical Economics, Springer, vol. 30(4), pages 947-971, January.
    20. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," The Journal of Business, University of Chicago Press, vol. 73(3), pages 477-491, July.
    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. Pyrlik, Vladimir, 2013. "Autoregressive conditional duration as a model for financial market crashes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6041-6051.
    2. Han, Ruokang & Takahashi, Taiki, 2012. "Psychophysics of time perception and valuation in temporal discounting of gain and loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6568-6576.

    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. dos Santos, Lindomar Soares & Destefano, Natália & Martinez, Alexandre Souto, 2018. "Decision making generalized by a cumulative probability weighting function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 250-259.
    2. Takahashi, Taiki, 2010. "A social discounting model based on Tsallis’ statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3600-3603.
    3. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    4. Destefano, Natália & Martinez, Alexandre Souto, 2011. "The additive property of the inconsistency degree in intertemporal decision making through the generalization of psychophysical laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(10), pages 1763-1772.
    5. Salvador Cruz Rambaud & María José Muñoz Torrecillas, 2016. "Measuring Impatience in Intertemporal Choice," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    6. Han, Ruokang & Takahashi, Taiki, 2012. "Psychophysics of time perception and valuation in temporal discounting of gain and loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6568-6576.
    7. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
    8. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    9. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Caporin, Massimiliano & Ranaldo, Angelo & Santucci de Magistris, Paolo, 2013. "On the predictability of stock prices: A case for high and low prices," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5132-5146.
    11. Natalia Destefano & Alexandre Souto Martinez, 2010. "The additive property of the inconsistency degree in intertemporal decision making through the generalization of psychophysical laws," Papers 1010.5648, arXiv.org, revised May 2011.
    12. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
    13. Richard D. F. Harris & Murat Mazibas, 2022. "A component Markov regime‐switching autoregressive conditional range model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 650-683, April.
    14. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    15. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    16. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2015. "The impact of financial crises on the risk–return tradeoff and the leverage effect," Economic Modelling, Elsevier, vol. 49(C), pages 407-418.
    17. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
    18. Jonathan J. Reeves & Xuan Xie, 2014. "Forecasting stock return volatility at the quarterly frequency: an evaluation of time series approaches," Applied Financial Economics, Taylor & Francis Journals, vol. 24(5), pages 347-356, March.
    19. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    20. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org, revised Sep 2024.

    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:eee:phsmap:v:391:y:2012:i:4:p:1489-1496. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.