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Shocks, Crashes and Bubbles in Financial Markets


  • Anders Johansen
  • Didier Sornette


In a series of papers based on analogies with statistical physics models, we have proposed that most financial crashes are the climax of so-called log-periodic power law signatures (LPPL) associated with speculative bubbles (Sornette and Johansen, 1998; Johansen and Sornette, 1999; Johansen et al. 1999; Johansen et al. 2000; Sornette and Johansen, 2001a). In addition, a large body of empirical evidence supporting this proposition have been presented (Sornette et al. 1996; Sornette and Johansen, 1998; Johansen et al. 2000; Johansen and Sornette, 2000; Johansen and Sornette, 2001a, Sornette and Johansen, 2001b). Along a complementary line of research, we have established that, while the vast majority of drawdowns occurring on the major financial markets have a distribution which is well-described by a stretched exponential, the largest drawdowns are occurring with a significantly larger rate than predicted by extrapolating the bulk of the distribution and should thus be considered as outliers (Johansen and Sornette, 1998; Sornette and Johansen, 2001; Johansen and Sornette, 2001; Johansen, 2002). Here, these two lines of research are merged in a systematic way to offer a classification of crashes as either events of an endogenous origin preceded by speculative bubbles or as events of exogenous origins associated to external shocks. We first perform an extended analysis of the distribution of drawdowns in the two leading exchange markets (US dollar against the Deutschmark 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. Then, for each identified outlier, we check whether LPPL are present and take the existence of LPPL 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 LPPL, 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 reasonable to attribute the crash to it, following 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 exogenous and 2 as associated with the Japanese “anti-bubble” starting in Jan. 1990. 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 LPPL, provides a novel and systematic taxonomy of crashes further substantiating the importance of LPPL. We stress that the proposed classification does not rule out the existence of other precursory signals in the absence of LPPL.

Suggested Citation

  • Anders Johansen & Didier Sornette, 2010. "Shocks, Crashes and Bubbles in Financial Markets," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 53(2), pages 201-253.
  • Handle: RePEc:bxr:bxrceb:2013/80942 Note: Numéro Spécial « Special Issue on Nonlinear Financial Analysis :Editorial Introduction » Guest Editor :Catherine Kyrtsou

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    References listed on IDEAS

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    Cited by:

    1. Vladimir Filimonov & Didier Sornette, 2014. "Power law scaling and "Dragon-Kings" in distributions of intraday financial drawdowns," Papers 1407.5037,, revised Apr 2015.
    2. T. Kaizoji & M. Leiss & A. Saichev & D. Sornette, 2011. "Super-exponential endogenous bubbles in an equilibrium model of rational and noise traders," Papers 1109.4726,, revised Mar 2014.
    3. 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.
    4. Maximilian Brauers & Matthias Thomas & Joachim Zietz, 2014. "Are There Rational Bubbles in REITs? New Evidence from a Complex Systems Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 49(2), pages 165-184, August.
    5. 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.
    6. Vladimir Filimonov & Didier Sornette, "undated". "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Working Papers ETH-RC-11-002, ETH Zurich, Chair of Systems Design.
    7. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    8. Zhang, Qunzhi & Sornette, Didier & Balcilar, Mehmet & Gupta, Rangan & Ozdemir, Zeynel Abidin & Yetkiner, Hakan, 2016. "LPPLS bubble indicators over two centuries of the S&P 500 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 126-139.
    9. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    10. Vladimir Filimonov & Didier Sornette, 2011. "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Papers 1108.0099,, revised Jun 2013.
    11. Gisler, Monika & Sornette, Didier & Woodard, Ryan, 2011. "Innovation as a social bubble: The example of the Human Genome Project," Research Policy, Elsevier, pages 1412-1425.
    12. Fry, John, 2012. "Exogenous and endogenous crashes as phase transitions in complex financial systems," MPRA Paper 36202, University Library of Munich, Germany.
    13. Yan, Wanfeng & Woodard, Ryan & Sornette, Didier, 2012. "Diagnosis and prediction of rebounds in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1361-1380.
    14. Vakhtina, Elena & Wosnitza, Jan Henrik, 2015. "Capital market based warning indicators of bank runs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 304-320.
    15. Kaizoji, Taisei & Leiss, Matthias & Saichev, Alexander & Sornette, Didier, 2015. "Super-exponential endogenous bubbles in an equilibrium model of fundamentalist and chartist traders," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 289-310.
    16. 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.
    17. Wosnitza, Jan Henrik & Denz, Cornelia, 2013. "Liquidity crisis detection: An application of log-periodic power law structures to default prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3666-3681.
    18. V. I. Yukalov & E. P. Yukalova & D. Sornette, 2015. "Dynamical system theory of periodically collapsing bubbles," Papers 1507.05311,

    More about this item


    Financial bubbles; Crashes; Super-exponential growth; Positive feedback; Power law; Log-periodicity; Prediction;

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets


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