LOGISTIC MODEL FOR STOCK MARKET BUBBLES AND ANTI-BUBBLES
Christopher Lynch () and
Benjamin Mestel ()
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Christopher Lynch: School of Mathematics & Statistics, The Open University, Milton Keynes, MK7 6AA, UK
Benjamin Mestel: School of Mathematics & Statistics, The Open University, Milton Keynes, MK7 6AA, UK
International Journal of Theoretical and Applied Finance (IJTAF), 2017, vol. 20, issue 06, 1-24
Log-periodic power laws often occur as signatures of impending criticality of hierarchical systems in the physical sciences. It has been proposed that similar signatures may be apparent in the price evolution of financial markets as bubbles and the associated crashes develop. The features of such market bubbles have been extensively studied over the past 20 years, and models derived from an initial discrete scale invariance assumption have been developed and tested against the wealth of financial data with varying degrees of success. In this paper, the equations that form the basis for the standard log-periodic power law model and its higher extensions are compared to a logistic model derived from the solution of the Schröder equation for the renormalization group with nonlinear scaling function. Results for the S&P 500 and Nikkei 225 indices studied previously in the literature are presented and compared to established models, including a discussion of the apparent frequency shifting observed in the S&P 500 index in the 1980s. In the particular case of the Nikkei 225 anti-bubble between 1990 and 2003, the logistic model appears to provide a better description of the large-scale observed features over the whole 13-year period, particularly near the end of the anti-bubble.
Keywords: Log-periodic power laws; renormalization group; financial bubbles; discrete scale invariance; criticality (search for similar items in EconPapers)
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