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Testing for financial crashes using the Log Periodic Power Law mode

Author

Listed:
  • David S. Bree

    (Institute for Scientific Interchange, Torino)

  • Nathan Lael Joseph

    (Aston University, Birmingham)

Abstract

A number of papers claim that a Log Periodic Power Law (LPPL) fitted to financial market bubbles that precede large market falls or 'crashes', contain parameters that are confined within certain ranges. The mechanism that has been claimed as underlying the LPPL, is based on influence percolation and a martingale condition. This paper examines these claims and the robustness of the LPPL for capturing large falls in the Hang Seng stock market index, over a 30-year period, including the current global downturn. We identify 11 crashes on the Hang Seng market over the period 1970 to 2008. The fitted LPPLs have parameter values within the ranges specified post hoc by Johansen and Sornette (2001) for only seven of these crashes. Interestingly, the LPPL fit could have predicted the substantial fall in the Hang Seng index during the recent global downturn. We also find that influence percolation combined with a martingale condition holds for only half of the pre-crash bubbles previously reported. Overall, the mechanism posited as underlying the LPPL does not do so, and the data used to support the fit of the LPPL to bubbles does so only partially.

Suggested Citation

  • David S. Bree & Nathan Lael Joseph, 2010. "Testing for financial crashes using the Log Periodic Power Law mode," Papers 1002.1010, arXiv.org, revised Apr 2013.
    • Handle: RePEc:arx:papers:1002.1010
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    Cited by:

    1. Christopher Lynch & Benjamin Mestel, 2017. "Logistic Model For Stock Market Bubbles And Anti-Bubbles," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(06), pages 1-24, September.
    2. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    3. Bikramaditya Ghosh & Spyros Papathanasiou & Nikita Ramchandani & Dimitrios Kenourgios, 2021. "Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes during BREXIT," Mathematics, MDPI, vol. 9(9), pages 1-14, April.
    4. Kwangwon Ahn & Hanwool Jang & Jinu Kim & Inug Ryu, 2024. "COVID-19 and REITs Crash: Predictability and Market Conditions," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1159-1172, March.
    5. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    6. Grobys, Klaus, 2023. "A finite-time singularity in the dynamics of the US equity market: Will the US equity market eventually collapse?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Hideyuki Takagi, 2021. "Exploring the Endogenous Nature of Meme Stocks Using the Log-Periodic Power Law Model and Confidence Indicator," Papers 2110.06190, arXiv.org.
    8. Fry, John, 2012. "Exogenous and endogenous crashes as phase transitions in complex financial systems," MPRA Paper 36202, University Library of Munich, Germany.
    9. Bikramaditya Ghosh & Spyros Papathanasiou & Vandita Dar & Dimitrios Kenourgios, 2022. "Deconstruction of the Green Bubble during COVID-19 International Evidence," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    10. Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    11. John Fry, 2014. "Bubbles, shocks and elementary technical trading strategies," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(1), pages 1-13, January.
    12. Hanwool Jang & Yena Song & Sungbin Sohn & Kwangwon Ahn, 2018. "Real Estate Soars and Financial Crises: Recent Stories," Sustainability, MDPI, vol. 10(12), pages 1-12, December.
    13. Gharib, Cheima & Mefteh-Wali, Salma & Serret, Vanessa & Ben Jabeur, Sami, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Resources Policy, Elsevier, vol. 74(C).
    14. Kristoffer Pons Bertelsen, 2019. "Comparing Tests for Identification of Bubbles," CREATES Research Papers 2019-16, Department of Economics and Business Economics, Aarhus University.
    15. Nathan Burks & Adetokunbo Fadahunsi & Ann Marie Hibbert, 2021. "Financial Contagion: A Tale of Three Bubbles," JRFM, MDPI, vol. 14(5), pages 1-14, May.
    16. Martin Herdegen & Sebastian Herrmann, 2017. "Strict Local Martingales and Optimal Investment in a Black-Scholes Model with a Bubble," Papers 1711.06679, arXiv.org.
    17. Vincenzo Liberatore, 2010. "Computational LPPL Fit to Financial Bubbles," Papers 1003.2920, arXiv.org, revised Jan 2011.
    18. Papastamatiou, Konstantinos & Karakasidis, Theodoros, 2022. "Bubble detection in Greek Stock Market: A DS-LPPLS model approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).

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