Testing for financial crashes using the Log Periodic Power Law model

David S. Brée, Nathan L. Joseph

Research output: Contribution to journalArticlepeer-review


Many papers claim that a Log Periodic Power Law (LPPL) model fitted to financial market bubbles that precede large market falls or 'crashes', contains parameters that are confined within certain ranges. Further, it is claimed that the underlying model is based on influence percolation and a martingale condition. This paper examines these claims and their validity for capturing large price falls in the Hang Seng stock market index over the period 1970 to 2008. The fitted LPPLs have parameter values within the ranges specified post hoc by Johansen and Sornette (2001) for only seven of these 11 crashes. Interestingly, the LPPL fit could have predicted the substantial fall in the Hang Seng index during the recent global downturn. Overall, the mechanism posited as underlying the LPPL model does not do so, and the data used to support the fit of the LPPL model to bubbles does so only partially.
Original languageEnglish
Pages (from-to)287-297
Number of pages11
JournalInternational Review of Financial Analysis
Early online date5 Jun 2013
Publication statusPublished - Dec 2013


  • financial time series
  • bubbles and crashes
  • nonlinear time series
  • robustness
  • log periodic power law


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