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

Michaelides, Panayotis G. and Tsionas, Efthymios and Konstantakis, Konstantinos (2016): Financial Bubble Detection : A Non-Linear Method with Application to S&P 500.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bubbles, Non-linearities, Neural Networks, EWM, S&P500
• Subjects: G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation; G - Financial Economics > G1 - General Financial Markets > G18 - Government Policy and Regulation

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