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Can we use volatility to diagnose financial bubbles? lessons from 40 historical bubbles

  • Received: 14 July 2017 Accepted: 05 November 2017 Published: 13 March 2018
  • JEL Codes: G14

  • We inspect the price volatility before, during, and after financial asset bubbles in order to uncover possible commonalities and check empirically whether volatility might be used as an indicator or an early warning signal of an unsustainable price increase and the associated crash. Some researchers and finance practitioners believe that historical and/or implied volatility increase before a crash, but we do not see this as a consistent behavior. We examine forty well-known bubbles and, using creative graphical representations to capture robustly the transient dynamics of the volatility, find that the dynamics of the volatility would not have been a useful predictor of the subsequent crashes. In approximately two-third of the studied bubbles, the crash follows a period of lower volatility, reminiscent of the idiom of a "lull before the storm". This paradoxical behavior, from the lenses of traditional asset pricing models, further questions the general relationship between risk and return.

    Citation: Didier Sornette, Peter Cauwels, Georgi Smilyanov. Can we use volatility to diagnose financial bubbles? lessons from 40 historical bubbles[J]. Quantitative Finance and Economics, 2018, 2(1): 486-590. doi: 10.3934/QFE.2018.1.1

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  • We inspect the price volatility before, during, and after financial asset bubbles in order to uncover possible commonalities and check empirically whether volatility might be used as an indicator or an early warning signal of an unsustainable price increase and the associated crash. Some researchers and finance practitioners believe that historical and/or implied volatility increase before a crash, but we do not see this as a consistent behavior. We examine forty well-known bubbles and, using creative graphical representations to capture robustly the transient dynamics of the volatility, find that the dynamics of the volatility would not have been a useful predictor of the subsequent crashes. In approximately two-third of the studied bubbles, the crash follows a period of lower volatility, reminiscent of the idiom of a "lull before the storm". This paradoxical behavior, from the lenses of traditional asset pricing models, further questions the general relationship between risk and return.


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