Dragon-Kings, Black Swans and the Prediction of Crises

CCSS Working Paper No. CCSS-09-005

20 Pages Posted: 1 May 2010

See all articles by Didier Sornette

Didier Sornette

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute

Multiple version iconThere are 2 versions of this paper

Date Written: July 24, 2009


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.

Keywords: outliers, kings, red dragons, extremes, crisis, catastrophes, bifurcations, power laws, prediction

Suggested Citation

Sornette, Didier, Dragon-Kings, Black Swans and the Prediction of Crises (July 24, 2009). CCSS Working Paper No. CCSS-09-005, Available at SSRN: https://ssrn.com/abstract=1596032 or http://dx.doi.org/10.2139/ssrn.1596032

Didier Sornette (Contact Author)

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
Zurich, ZURICH CH-8092
41446328917 (Phone)
41446321914 (Fax)

HOME PAGE: http://www.er.ethz.ch/

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics