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Probabilistic forecasting of bubbles and flash crashes

Author

Listed:
  • Anurag Banerjee
  • Guillaume Chevillon
  • Marie Kratz

Abstract

SummaryWe propose a near-explosive random coefficient autoregressive model (NERC) to obtain predictive probabilities of the apparition and devolution of bubbles. The distribution of the autoregressive coefficient of this model is allowed to be centred at an O(T−α) distance of unity, with α ∈ (0, 1). When the expectation of the autoregressive coefficient lies on the explosive side of unity, the NERC helps to model the temporary explosiveness of time series and obtain related predictive probabilities. We study the asymptotic properties of the NERC and provide a procedure for inference on the parameters. In empirical illustrations, we estimate predictive probabilities of bubbles or flash crashes in financial asset prices.

Suggested Citation

  • Anurag Banerjee & Guillaume Chevillon & Marie Kratz, 2020. "Probabilistic forecasting of bubbles and flash crashes," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 297-315.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:2:p:297-315.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa004
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    Cited by:

    1. Nishi, Mikihito & 西, 幹仁 & Kurozumi, Eiji & 黒住, 英司, 2022. "Stochastic Local and Moderate Departures from a Unit Root and Its Application to Unit Root Testing," Discussion Papers 2022-02, Graduate School of Economics, Hitotsubashi University.

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