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Non-linearities in financial bubbles: Theory and Bayesian evidence from S&P500

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  • Michaelides, Panayotis G.
  • Tsionas, Efthymios G.
  • Konstantakis, Konstantinos N.

Abstract

The modeling process of bubbles, using advanced mathematical and econometric techniques, is a young field of research. In this context, significant model misspecification could result from ignoring potential non-linearities. More precisely, the present paper attempts to detect and date non-linear bubble episodes. To do so, we use Neural Networks to capture the neglected non-linearities. Also, we provide a recursive dating procedure for bubble episodes. When using data on stock price-dividend ratio S&P500 (1871.1–2014.6), employing Bayesian techniques, the proposed approach identifies more episodes than other bubble tests in the literature, while the common episodes are, in general, found to have a longer duration, which is evidence of an early warning mechanism (EWM) that could have important policy implications.

Suggested Citation

  • Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2016. "Non-linearities in financial bubbles: Theory and Bayesian evidence from S&P500," Journal of Financial Stability, Elsevier, vol. 24(C), pages 61-70.
    • Handle: RePEc:eee:finsta:v:24:y:2016:i:c:p:61-70
    DOI: 10.1016/j.jfs.2016.04.007
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    More about this item

    Keywords

    Non-linearities; Bubbles; Neural Networks; Early detection; S&P500;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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