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A new mechanism for anticipating price exuberance

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  • Moreira, Afonso M.
  • Martins, Luis F.

Abstract

It is very important for investors, market regulators, and policy makers to possess a trustworthy ex-ante tool capable of anticipating price exuberance events. This paper proposes a new statistical mechanism to predict speculative bubbles by inferring a significant probability of exuberance at least one step ahead of a bubble peak period. Contrary to other approaches, we combine asset pricing modeling and non-stationarity statistical analysis and use both in the context of adaptive learning to build a dynamic model specification. Monte Carlo simulations show that the ex-ante prediction is improved enormously by adding the estimated abnormal returns into the model. In some cases our mechanism predicts 100% of the last bubbles of the sample up to five periods before the peak. Furthermore, the mechanism is able to successfully anticipate the technological bubble observed in the 1990’s by estimating a probability greater than 90%, one month before the bubble peak. Thus, this new mechanism provides an advantage for investors interested in performing a very profitable “bubble surfing” strategy and for market regulators whose responsibility is to maintain market efficiency.

Suggested Citation

  • Moreira, Afonso M. & Martins, Luis F., 2020. "A new mechanism for anticipating price exuberance," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 199-221.
  • Handle: RePEc:eee:reveco:v:65:y:2020:i:c:p:199-221
    DOI: 10.1016/j.iref.2019.10.006
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    More about this item

    Keywords

    Speculative bubbles; Asset pricing; Non-stationarity; Adaptive learning; Dynamic models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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