Detecting global financial crises with scarce data by multivariate nonlinear filtering - Cécile Bastidon; Antoine Parent; Patrice Abry; Pierre Borgnat; Pablo Jensen; Barbara Pascal (Journal of Physics: Complexity, 2025)
An original procedure is devised for the automated detection of global financial crises from multivariate databases of share prices. It consists of: i) the construction of time series from the time-windowed estimations of crisis relevant information (cross-correlations or volatilities); ii) the piecewise-linear filtering of times series by nonlinear filtering, achieved by nonsmooth proximal minimization implemented by an efficient iterative algorithm; iii) the estimation of a reassigned time in each window; iv) the detection of crises and estimation of their intensities by exploiting the multivariate structure of denoised time series. Applied to a world dataset of 32 indices over 6 decades, this original model based procedure detects all major crises from the reference lists. It also permits to devise a typology in reference to an archetypal financial crisis. It is automated, data-driven and reproducible notably for the analysis of financial crises over history, or contemporary crises on worldwide databases, via a novel toolbox. Finally it is robust to scarce, incomplete and noisy data.