Dragon-Kings, Black Swans and the Prediction of Crises

By Didier Sornette. 2009

318 citations recorded by the source corpus.

We develop the concept of “dragon-kings” corresponding to meaningful outliers, which are found to coexist with power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems. These dragon-kings reveal the existence of mechanisms of self-organization that are not apparent otherwise from the distribution of their smaller siblings. We present a generic phase diagram to explain the generation of dragon-kings and document their presence in six different examples (distribution of city sizes, distribution of acoustic emissions associated with material failure, distribution of velocity increments in hydrodynamic turbulence, distribution of financial drawdowns, distribution of the energies of epileptic seizures in humans and in model animals, distribution of the earthquake energies). We emphasize the importance of understanding dragon-kings as being often associated with a neighborhood of what can be called equivalently a phase transition, a bifurcation, a catastrophe (in the sense of Rene Thom), or a tipping point. The presence of a phase transition is crucial to learn how to diagnose in advance the symptoms associated with a coming dragon-king. Several examples of predictions using the derived log-periodic power law method are discussed, including material failure predictions and the forecasts of the end of financial bubbles.

Subfields: Economics and Econometrics, Geophysics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e02d4da307dbf7ffafb49b8d3c3cc5e010b3f146

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Citations in the corpus

Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model (2011, background): “nancial system. In the abstract of the reference [41], Johansen and Sornette state: “Globally over all the markets analyzed, we identify 49 outliers (now referred more appropriately as “dragon-kings” [42]), of which 25 are classiﬁed as endogenous, 22 as exogenous and 2 as associated with the Japanes”
Dragon-kings: Mechanisms, statistical methods and empirical evidence (2012, unknown): “Delayed feedbacks to control instabilities often give rise to power law distribution of the amplitude of the control step.”
Understanding Compound, Interconnected, Interacting, and Cascading Risks: A Holistic Framework (2018, background): “After a long debate on unknown, low-probability, and high-impact events, it has been suggested that extreme scenarios could be more common than was previously supposed, and that this requires us to develop a new understanding of their drivers (Sornette, 2009).”
Power laws and Self-Organized Criticality in Theory and Nature (2013, background): “nd to dierentiate between dragon kings and a regular large-scale events. Tools and methods used for the identication of dragon kings 46 often depend on the particularities of the system in question [147, 151]. Only recently had Pisarenko and Sornette [128] proposed a robust statistical test able to ”
Shocks, Crashes and Bubbles in Financial Markets (2010, background): “…show that the largest drawdowns in general are outliers to the vast majority ( %98 ) of drawdowns and cannot be described by an extrapolation of the distribution of small and intermediate losses, a property that we refer to as the “dragon-king” effect (Laherrère and Sornette, 1998; Sornette, 2009).”
Prediction (2010, unknown): “In the context of system predictability,it has profound implications.”