IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v10y2022i1p14-d716887.html
   My bibliography  Save this article

Log Periodic Power Analysis of Critical Crashes: Evidence from the Portuguese Stock Market

Author

Listed:
  • Tiago Cruz Gonçalves

    (Advance/CSG, ISEG—Lisbon School of Economics & Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal)

  • Jorge Victor Quiñones Borda

    (Facultad de Ciencias Económicas, Unidad de Posgrado, Ciudad Universitaria, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru)

  • Pedro Rino Vieira

    (Advance/CSG, ISEG—Lisbon School of Economics & Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal)

  • Pedro Verga Matos

    (Advance/CSG, ISEG—Lisbon School of Economics & Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal)

Abstract

The study of critical phenomena that originated in the natural sciences has been extended to the financial economics’ field, giving researchers new approaches to risk management, forecasting, the study of bubbles and crashes, and many kinds of problems involving complex systems with self-organized criticality (SOC). This study uses the theory of self-similar oscillatory time singularities to analyze stock market crashes. We test the Log Periodic Power Law/Model (LPPM) to analyze the Portuguese stock market, in its crises in 1998, 2007, and 2015. Parameter values are in line with those observed in other markets. This is particularly interesting since if the model performs robustly for Portugal, which is a small market with liquidity issues and the index is only composed of 20 stocks, we provide consistent evidence in favor of the proposed LPPM methodology. The LPPM methodology proposed here would have allowed us to avoid big loses in the 1998 Portuguese crash, and would have permitted us to sell at points near the peak in the 2007 crash. In the case of the 2015 crisis, we would have obtained a good indication of the moment where the lowest data point was going to be achieved.

Suggested Citation

  • Tiago Cruz Gonçalves & Jorge Victor Quiñones Borda & Pedro Rino Vieira & Pedro Verga Matos, 2022. "Log Periodic Power Analysis of Critical Crashes: Evidence from the Portuguese Stock Market," Economies, MDPI, vol. 10(1), pages 1-19, January.
  • Handle: RePEc:gam:jecomi:v:10:y:2022:i:1:p:14-:d:716887
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/10/1/14/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7099/10/1/14/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sornette, Didier & Johansen, Anders, 1997. "Large financial crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 411-422.
    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. Zitis, Pavlos I. & Contoyiannis, Yiannis & Potirakis, Stelios M., 2022. "Critical dynamics related to a recent Bitcoin crash," International Review of Financial Analysis, Elsevier, vol. 84(C).

    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. Anders Johansen & Didier Sornette & Olivier Ledoit, 1999. "Empirical and Theoretical Status of Discrete Scale Invariance in Financial Crashes," Finance 9903006, University Library of Munich, Germany.
    2. Wong, Jian Cheng & Lian, Heng & Cheong, Siew Ann, 2009. "Detecting macroeconomic phases in the Dow Jones Industrial Average time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(21), pages 4635-4645.
    3. Sornette, Didier & Johansen, Anders, 1998. "A hierarchical model of financial crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 261(3), pages 581-598.
    4. Johansen, Anders, 2003. "Characterization of large price variations in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 157-166.
    5. Marco Airoldi & Vito Antonelli & Bruno Bassetti & Andrea Martinelli & Marco Picariello, 2004. "Long Range Interaction Generating Fat-Tails in Finance," GE, Growth, Math methods 0404006, University Library of Munich, Germany, revised 27 Apr 2004.
    6. Yu Zhang & Xiaosong Zheng, 2016. "A Study of Herd Behavior Based on the Chinese Stock Market," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 5(2), pages 131-135, May.
    7. B. M. Roehner & D. Sornette, 2000. ""Thermometers" of Speculative Frenzy," Papers cond-mat/0001353, arXiv.org.
    8. Didier Sornette & Wei-Xing Zhou, 2003. "The US 2000-2002 market descent: clarification," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 39-41.
    9. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Can log-periodic power law structures arise from random fluctuations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 228-250.
    10. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    11. Habtemicael, Semere & SenGupta, Indranil, 2014. "Ornstein–Uhlenbeck processes for geophysical data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 147-156.
    12. Gregory G. Brunk, 2003. "Swarming of innovations, fractal patterns, and the historical time series of US patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 61-80, January.
    13. Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2012. "A method for detection of abrupt changes in the financial market combining wavelet decomposition and correlation graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4877-4882.
    14. Domino, Krzysztof, 2012. "The use of the Hurst exponent to investigate the global maximum of the Warsaw Stock Exchange WIG20 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 156-169.
    15. Mariani, Maria C. & Basu, Kanadpriya, 2015. "Spline interpolation techniques applied to the study of geophysical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 68-79.
    16. Xingxing Ye & Raphaël Douady, 2019. "Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02488592, HAL.
    17. Zhou, Wei-Xing & Sornette, Didier, 2005. "Testing the stability of the 2000 US stock market “antibubble”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 428-452.
    18. Petr Geraskin & Dean Fantazzini, 2013. "Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 366-391, May.
    19. Qin Xiao & Gee Kwang Randolph Tan, 2007. "Signal Extraction with Kalman Filter: A Study of the Hong Kong Property Price Bubbles," Urban Studies, Urban Studies Journal Limited, vol. 44(4), pages 865-888, April.
    20. John M. Fry, 2009. "Statistical modelling of financial crashes: Rapid growth, illusion of certainty and contagion," EERI Research Paper Series EERI_RP_2009_10, Economics and Econometrics Research Institute (EERI), Brussels.

    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:gam:jecomi:v:10:y:2022:i:1:p:14-:d:716887. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.