Critical slowing down as an early warning signal for financial crises

The global impact of the recent financial crisis has once more stressed the urgency of new approaches to designing early warning signals (EWS) for financial crises. In the recent literature on constructing EWS through identifying characteristics of critical slowdown on the basis of time series observations, finance has repeatedly been coined as an important potential application area. On the one hand, this appealing idea is supported by the fact that there is ample empirical and experimental evidence to suggest that nonlinearities play a role in the expectations feedback governing market dynamics. On the other hand, financial markets differ from many natural complex systems, for which evidence of critical slowing down has been reported, in that market dynamics are not necessarily captured well by an ordinary differential equation, the fixed point of which may lose stability through a saddle-node bifurcation, as is the case for the cusp catastrophe. Also, financial time series exhibit persistent near unit root behaviour. In this paper we consider a number of historical financial crises, to investigate wheather there is indeed evidence for critical slowing down prior to market collapses. The four events considered are Black Monday 1987, the 1997 Asian Crisis, the 2000 Dot.com bubble burst, andthe 2008 Financial Crisis. Our analysis shows evidence for critical slowing down before Black Monday 1987, while the results are mixed and insignificant for the other financial crises.