Predicting Financial Market Crashes Using Ghost Singularities

Damian Smug, Peter Ashwin and Didier Sornette
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Damian Smug: University of Exeter
Peter Ashwin: University of Exeter
Didier Sornette: ETH Zürich and Swiss Finance Institute

No 17-23, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We analyse the behaviour of a non-linear model of coupled stock and bond prices exhibiting periodically collapsing bubbles. By using the formalism of dynamical system theory, we explain what drives the bubbles and how foreshocks or aftershocks are generated. A dynamical phase space representation of that system coupled with standard multiplicative noise rationalises the log-periodic power law singularity pattern documented in many historical financial bubbles. The notion of ‘ghosts of finite-time singularities’ is introduced and used to estimate the end of an evolving bubble, using finite-time singularities of an approximate normal form near the bifurcation point. We test the forecasting skill of this method on different stochastic price realisations and compare with Monte Carlo simulations of the full system. Remarkably, the former is significantly more precise and less biased. Moreover, the method of ghosts of singularities is less sensitive to the noise realisation, thus providing more robust forecasts.

Keywords: Financial Markets; State Space Models; Price Forecasting; Simulation; Bifurcation Theory; Finite-Time Singularity (search for similar items in EconPapers)
JEL-codes: C02 C18 C32 G01 G17 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2017-06
New Economics Papers: this item is included in nep-hme
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1723

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