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The timing of the popping: using the log-periodic power law model to predict the bursting of bubbles on financial markets

Published online by Cambridge University Press:  22 August 2016

Marcus Gustavsson ,
Daniel Levén  and
Hans Sjögren
Marcus Gustavsson
Affiliation:
Linköping University
Daniel Levén
Affiliation:
Linköping University
Hans Sjögren*
Affiliation:
Linköping University
*
Marcus Gustavsson, Daniel Levén and Hans Sjögren, Department of Management and Engineering, Linköping University, SE-581 83 Linköping, Sweden. Corresponding author: Hans Sjögren, email hans.sjogren@liu.se
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Abstract

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. We examine the ability of the log-periodic power law model (LPPL-model) to accurately predict the end dates of speculative bubbles on financial markets through modeling of asset price dynamics on a selection of historical bubbles. The method is based on a nonlinear least squares estimation that yields predictions of when the bubble will change regime. Previous studies have only presented results where the predictions turn out to be successful. This study is the first to highlight both the potential and the limitations of the LPPL-model. We find evidence that supports the characteristic patterns as proposed by the LPPL-framework leading up to the change in regime; asset prices during bubble periods seem to oscillate around a faster-than-exponential growth. In most cases the estimation yields accurate predictions, although we conclude that the predictions are quite dependent on the point in time at which they are conducted. We also find that the end of a speculative bubble seems to be influenced by both endogenous speculative growth and exogenous factors. For this reason we propose a new way of interpreting the predictions of the model, where the end dates should be interpreted as the start of a time period where the asset prices are especially sensitive to exogenous events. We propose that negative news during this time period results in a regime shift and the bursting of the bubble. Thus, the model has the ability to predict sensitivity to exogenous events ex ante.

Keywords

bubble forecastingfinancial crisisstock market crashlog-periodic power law model (LPPL-model)asset price dynamics

JEL classification

G01: Financial Crises G12: Asset Pricing • Trading Volume • Bond Interest Rates G17: Financial Forecasting and Simulation N20: General, International, or Comparative
Type
Articles
Information
Financial History Review , Volume 23 , Issue 2 , August 2016 , pp. 193 - 217
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Copyright
Copyright © European Association for Banking and Financial History e.V. 2016 

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References

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The Economist Google Scholar

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