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Origin of Crashes in 3 US stock markets: Shocks and Bubbles

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  • Anders Johansen

Abstract

This paper presents an exclusive classification of the largest crashes in Dow Jones Industrial Average (DJIA), SP500 and NASDAQ in the past century. Crashes are objectively defined as the top-rank filtered drawdowns (loss from the last local maximum to the next local minimum disregarding noise fluctuations), where the size of the filter is determined by the historical volatility of the index. It is shown that {\it all} crashes can be linked to either an external shock, {\it e.g.}, outbreak of war, {\it or} a log-periodic power law (LPPL) bubble with an empirically well-defined complex value of the exponent. Conversely, with one sole exception {\it all} previously identified LPPL bubbles are followed by a top-rank drawdown. As a consequence, the analysis presented suggest a one-to-one correspondence between market crashes defined as top-rank filtered drawdowns on one hand and surprising news and LPPL bubbles on the other. We attribute this correspondence to the Efficient Market Hypothesis effective on two quite different time scales depending on whether the market instability the crash represent is internally or externally generated.

Suggested Citation

  • Anders Johansen, 2004. "Origin of Crashes in 3 US stock markets: Shocks and Bubbles," Papers cond-mat/0401210, arXiv.org.
  • Handle: RePEc:arx:papers:cond-mat/0401210
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    Cited by:

    1. George Chang & James Feigenbaum, 2006. "A Bayesian analysis of log-periodic precursors to financial crashes," Quantitative Finance, Taylor & Francis Journals, vol. 6(1), pages 15-36.
    2. Riccardo Rebonato & Valerio Gaspari, 2006. "Analysis of drawdowns and drawups in the US$ interest-rate market," Quantitative Finance, Taylor & Francis Journals, vol. 6(4), pages 297-326.
    3. Sanjay Rajagopal & Patrick Hays, 2012. "Return Persistence in the Indian Real Estate Market," International Real Estate Review, Global Social Science Institute, vol. 15(3), pages 283-305.
    4. Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    5. Fry, J. M., 2009. "Statistical modelling of financial crashes: Rapid growth, illusion of certainty and contagion," MPRA Paper 16027, University Library of Munich, Germany.
    6. Fry, J. M., 2010. "Gaussian and non-Gaussian models for financial bubbles via econophysics," MPRA Paper 27307, University Library of Munich, Germany.
    7. Vygodina, Anna V. & Zorn, Thomas S. & DeFusco, Richard, 2008. "Asymmetry in the effects of economic fundamentals on rising and falling exchange rates," International Review of Financial Analysis, Elsevier, vol. 17(4), pages 728-746, September.
    8. Cajueiro, Daniel O. & Tabak, Benjamin M. & Werneck, Filipe K., 2009. "Can we predict crashes? The case of the Brazilian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1603-1609.
    9. Riccardo Rebonato & Jian Chen, 2009. "Evidence for state transition and altered serial codependence in US$ interest rates," Quantitative Finance, Taylor & Francis Journals, vol. 9(3), pages 259-278.
    10. L. Lin & Ren R. E & D. Sornette, 2009. "A Consistent Model of `Explosive' Financial Bubbles With Mean-Reversing Residuals," Papers 0905.0128, arXiv.org.
    11. Rotundo, Giulia & Navarra, Mauro, 2007. "On the maximum drawdown during speculative bubbles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 235-246.
    12. Fry, J. M., 2010. "Bubbles and crashes in finance: A phase transition from random to deterministic behaviour in prices," MPRA Paper 24778, University Library of Munich, Germany.
    13. Panagiotis Papaioannnou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Papers 1310.5306, arXiv.org.
    14. Lin, L. & Ren, R.E. & Sornette, D., 2014. "The volatility-confined LPPL model: A consistent model of ‘explosive’ financial bubbles with mean-reverting residuals," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 210-225.
    15. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.
    16. L. Lin & Ren R.E. & D. Sornette, "undated". "A Consistent Model of `Explosive' Financial Bubbles With Mean-Reversing Residuals," Working Papers CCSS-09-002, ETH Zurich, Chair of Systems Design.

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