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Power Law Scaling and 'Dragon-Kings' in Distributions of Intraday Financial Drawdowns

Author

Listed:
  • Vladimir FILIMONOV

    (University of Zurich)

  • Didier SORNETTE

    (ETH Zurich and Swiss Finance Institute)

Abstract

We investigate the distributions of e-drawdowns and e-drawups of the most liquid futures financial contracts of the world at time scales of 30 seconds. The e-drawdowns (resp. e-drawups) generalise the notion of runs of negative (resp. positive) returns so as to capture the risks to which investors are arguably the most concerned with. Similarly to the distribution of returns, we find that the distributions of e-drawdowns and e-drawups exhibit power law tails, albeit with exponents significantly larger than those for the return distributions. This paradoxical result can be attributed to (i) the existence of significant transient dependence between returns and (ii) the presence of large outliers (dragon-kings) characterizing the extreme tail of the drawdown/drawup distributions deviating from the power law. The study of the tail dependence between the sizes, speeds and durations of drawdown/drawup indicates a clear relationship between size and speed but none between size and duration. This implies that the most extreme drawdown/drawup tend to occur fast and are dominated by a few very large returns. We discuss both the endogenous and exogenous origins of these extreme events.

Suggested Citation

  • Vladimir FILIMONOV & Didier SORNETTE, 2014. "Power Law Scaling and 'Dragon-Kings' in Distributions of Intraday Financial Drawdowns," Swiss Finance Institute Research Paper Series 14-48, Swiss Finance Institute, revised Apr 2015.
    • Handle: RePEc:chf:rpseri:rp1448
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    File URL: http://ssrn.com/abstract=2468195
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    Citations

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    Cited by:

    1. Junqing Tang & Hans R. Heinimann, 2019. "Quantitative evaluation of consecutive resilience cycles in stock market performance: A systems-oriented approach," Papers 1903.03201, arXiv.org.
    2. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & AL-Dhurafi, Nasr Ahmed, 2020. "The power-law distribution for the income of poor households," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. 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).
    4. Takumi Sueshige & Didier Sornette & Hideki Takayasu & Misako Takayasu, 2019. "Classification of position management strategies at the order-book level and their influences on future market-price formation," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    5. Thangavel, Bhagyaraj & Srinivasan, Sabarathinam & Kathamuthu, Thamilmaran, 2021. "Extreme events in a forced BVP oscillator: Experimental and numerical studies," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    6. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "Optimal threshold for Pareto tail modelling in the presence of outliers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 169-180.
    7. Fruehwirt, Wolfgang & Hochfilzer, Leonhard & Weydemann, Leonard & Roberts, Stephen, 2021. "Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis," Finance Research Letters, Elsevier, vol. 40(C).
    8. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2019. "A robust and efficient estimator for the tail index of inverse Pareto distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 431-439.

    More about this item

    Keywords

    Extreme events; drawdowns; power law distribution; tail dependence; Dragon-King events; financial markets; high-frequency data;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • G01 - Financial Economics - - General - - - Financial Crises

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