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

Panayotis Michaelides, Mike Tsionas and Konstantinos Konstantakis

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Bubbles; Non-linearities; Neural Networks; EWM; S&P500 (search for similar items in EconPapers)
JEL-codes: G01 G17 G18 (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-cmp and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:74477

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