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

Panayotis Michaelides, Mike Tsionas and Konstantinos Konstantakis

Journal of Financial Stability, 2016, vol. 24, issue C, 61-70

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.

Keywords: Non-linearities; Bubbles; Neural Networks; Early detection; S&P500 (search for similar items in EconPapers)
JEL-codes: C5 G1 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:24:y:2016:i:c:p:61-70

DOI: 10.1016/j.jfs.2016.04.007

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