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

Michaelides, Panayotis G. and Tsionas, Efthymios and Konstantakis, Konstantinos N. (2016) Non-linearities in financial bubbles:theory and Bayesian evidence from S&P500. Journal of Financial Stability, 24. pp. 61-70. ISSN 1572-3089

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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 nonlinearities and, hence, it would seem wise to ensure that no terms with explanatory power are neglected. 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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Financial Stability
Additional Information:
This is the author’s version of a work that was accepted for publication in Journal of Financial Stability. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Financial Stability, 24, 2016 DOI: 10.1016/j.jfs.2016.04.007
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Deposited On:
03 Jun 2016 15:00
Last Modified:
20 Sep 2023 00:52