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Financial Bubble Detection : A Non-Linear Method with Application to S&P 500

Author

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

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 tocapture 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 otherbubble 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) thatcouldhave important policy implications.

Suggested Citation

  • Michaelides, Panayotis G. & Tsionas, Efthymios & Konstantakis, Konstantinos, 2016. "Financial Bubble Detection : A Non-Linear Method with Application to S&P 500," MPRA Paper 74477, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:74477
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

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

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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