IDEAS home Printed from https://ideas.repec.org/r/stz/wpaper/eth-rc-12-004.html
   My bibliography  Save this item

Dragon-kings: Mechanisms, statistical methods and empirical evidence

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Sergey Bredikhin & Jonathan Linton & Thais Matoszko, 2017. "Why and How the Value of Science-Based Firms Violates Financial Theory: Implications for Policy and Governance," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 11(1), pages 24-30.
  2. Arnaud Mignan & Stefan Wiemer & Domenico Giardini, 2014. "The quantification of low-probability–high-consequences events: part I. A generic multi-risk approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(3), pages 1999-2022, September.
  3. Phillips, Emir, 2019. "Nassim Taleb heads international banking’s first Grey/Black Swan Committee," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 117-122.
  4. Jerome L Kreuser & Didier Sornette, 2017. "Super-Exponential RE Bubble Model with Efficient Crashes," Swiss Finance Institute Research Paper Series 17-33, Swiss Finance Institute.
  5. Sinha, Amit & Horvath, Philip A. & Beason, Tyler & Roos, Kelly R., 2019. "Simulation of a financial market: The possibility of catastrophic disequilibrium," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 13-16.
  6. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2017. "Emerging interdependence between stock values during financial crashes," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
  7. Baggio, Rodolfo, 2015. "Looking into the future of complex dynamic systems," MPRA Paper 65549, University Library of Munich, Germany.
  8. Rebecca Westphal & Didier Sornette, 2020. "How market intervention can prevent bubbles and crashes," Swiss Finance Institute Research Paper Series 20-74, Swiss Finance Institute.
  9. Timothy Davies, 2015. "Developing resilience to naturally triggered disasters," Environment Systems and Decisions, Springer, vol. 35(2), pages 237-251, June.
  10. Masahiro Sugiyama & Hiroshi Deguchi & Arisa Ema & Atsuo Kishimoto & Junichiro Mori & Hideaki Shiroyama & Roland W. Scholz, 2017. "Unintended Side Effects of Digital Transition: Perspectives of Japanese Experts," Sustainability, MDPI, vol. 9(12), pages 1-20, November.
  11. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
  12. Filimonov, Vladimir & Sornette, Didier, 2015. "Power law scaling and “Dragon-Kings” in distributions of intraday financial drawdowns," Chaos, Solitons & Fractals, Elsevier, vol. 74(C), pages 27-45.
  13. Faggini, Marisa & Bruno, Bruna & Parziale, Anna, 2019. "Crises in economic complex networks: Black Swans or Dragon Kings?," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 105-115.
  14. Fry, John & Serbera, Jean-Philippe & Wilson, Rob, 2021. "Managing performance expectations in association football," Journal of Business Research, Elsevier, vol. 135(C), pages 445-453.
  15. Didier Sornette & Spencer Wheatley & Peter Cauwels, 2019. "The fair reward problem: the illusion of success and how to solve it," Papers 1902.04940, arXiv.org, revised Apr 2019.
  16. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
  17. Medina, José M. & Díaz, José A., 2016. "Extreme reaction times determine fluctuation scaling in human color vision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 125-132.
  18. Biton, Dionessa C. & Tarun, Anjali B. & Batac, Rene C., 2020. "Comparing spatio-temporal networks of intermittent avalanche events: Experiment, model, and empirical data," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
  19. Arnaud Mignan & Laurentiu Danciu & Domenico Giardini, 2018. "Considering large earthquake clustering in seismic risk analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 149-172, April.
  20. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2016. "Emerging interdependence between stock values during financial crashes," Papers 1611.02549, arXiv.org.
  21. 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.
  22. Sonntag, Dominik, 2018. "Die Theorie der fairen geometrischen Rendite [The Theory of Fair Geometric Returns]," MPRA Paper 87082, University Library of Munich, Germany.
  23. Glette-Iversen, Ingrid & Aven, Terje, 2021. "On the meaning of and relationship between dragon-kings, black swans and related concepts," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
  24. S. Z. Stefanov & Paul P. Wang, 2018. "Taming the Dragon-King of a Day-Ahead Smart Grid Blackout," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 11-20, March.
  25. Jan-Christian Gerlach & Jerome Kreuser & Didier Sornette, 2020. "Awareness of crash risk improves Kelly strategies in simulated financial time series," Papers 2004.09368, arXiv.org.
  26. Vladimir Filimonov & Didier Sornette, 2014. "Power law scaling and "Dragon-Kings" in distributions of intraday financial drawdowns," Papers 1407.5037, arXiv.org, revised Apr 2015.
  27. Didier Sornette & Spencer Wheatley & Peter Cauwels, 2019. "The Fair Reward Problem: The Illusion Of Success And How To Solve It," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-52, May.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.