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Endogenous versus Exogenous Crashes in Financial Markets

Author

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  • A. Johansen

    (Riso National Lab., Denmark)

  • D. Sornette

    (UCLA and CNRS-Univ. Nice)

Abstract

We perform an extended analysis of the distribution of drawdowns in the two leading exchange markets (US dollar against the Deutsmark and against the Yen), in the major world stock markets, in the U.S. and Japanese bond market and in the gold market, by introducing the concept of ``coarse-grained drawdowns,'' which allows for a certain degree of fuzziness in the definition of cumulative losses and improves on the statistics of our previous results on the existence of ``outliers'' or ``kings.'' Then, for each identified outlier, we check whether log-periodic power law signatures (LPPS) are present and take the existence of LPPS as the qualifying signature for an endogenous crash: this is because a drawdown outlier is seen as the end of a speculative unsustainable accelerating bubble generated endogenously. In the absence of LPPS, we are able to identify what seems to have been the relevant historical event, i.e., a new piece of information of such magnitude and impact that it is seems reasonable to attribute the crash to it, in agreement with the standard view of the efficient market hypothesis. Such drawdown outliers are classified as having an exogenous origin. Globally over all the markets analyzed, we identify 49 outliers, of which 25 are classified as endogenous, 22 as exogeneous and 2 as associated with the Japanese anti-bubble. Restricting to the world market indices, we find 31 outliers, of which 19 are endogenous, 10 are exogenous and 2 are associated with the Japanese anti-bubble. The combination of the two proposed detection techniques, one for drawdown outliers and the second for LPPS, provides a novel and systematic taxonomy of crashes further subtantiating the importance of LPPS.

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  • A. Johansen & D. Sornette, 2002. "Endogenous versus Exogenous Crashes in Financial Markets," Papers cond-mat/0210509, arXiv.org.
  • Handle: RePEc:arx:papers:cond-mat/0210509
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    References listed on IDEAS

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    2. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Papers cond-mat/0106520, arXiv.org.
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    14. D. Sornette & Y. Malevergne & J. F. Muzy, 2002. "Volatility fingerprints of large shocks: Endogeneous versus exogeneous," Papers cond-mat/0204626, arXiv.org.
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    Cited by:

    1. Didier Sornette & Wei-Xing Zhou, 2003. "The US 2000-2002 market descent: clarification," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 39-41.
    2. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2015. "Markets, herding and response to external information," Papers 1506.03708, arXiv.org, revised Jun 2015.
    3. Tanya Araujo & Francisco Louca, 2007. "The geometry of crashes. A measure of the dynamics of stock market crises," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 63-74.
    4. Ma, Rong & Zhang, Yin & Li, Honggang, 2017. "Traders’ behavioral coupling and market phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 618-627.
    5. Alvarez-Ramirez, Jose & Ibarra-Valdez, Carlos, 2004. "Finite-time singularities in the dynamics of Mexican financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 331(1), pages 253-268.
    6. Adrián Carro & Raúl Toral & Maxi San Miguel, 2015. "Markets, Herding and Response to External Information," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    7. Jeong-Ryeol Kurz-Kim, 2012. "Early warning indicator for financial crashes using the log periodic power law," Applied Economics Letters, Taylor & Francis Journals, vol. 19(15), pages 1465-1469, October.
    8. Boon Kin Teh & Siew Ann Cheong, 2016. "The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-13, October.
    9. 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.
    10. Zhou, Wei-Xing & Sornette, Didier, 2003. "Renormalization group analysis of the 2000–2002 anti-bubble in the US S&P500 index: explanation of the hierarchy of five crashes and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 330(3), pages 584-604.
    11. Tanya Ara'ujo & Francisco Louc{c}~a, 2005. "The Geometry of Crashes - A Measure of the Dynamics of Stock Market Crises," Papers physics/0506137, arXiv.org, revised Jul 2005.

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