Dynamic Score-Driven Independent Component Analysis

A model for dynamic independent component analysis is introduced where the dynamics are driven by the score of the pseudo likelihood with respect to the rotation angle of model innovations. While conditional second moments are invariant with respect to rotations, higher conditional moments are not, which may have important implications for applications. The pseudo maximum likelihood estimator of the model is shown to be consistent and asymptotically normally distributed. A simulation study reports good finite sample properties of the estimator, including the case of a misspecification of the innovation density. In an application to a bivariate exchange rate series of the Euro and the British Pound against the U.S. Dollar, it is shown that the model-implied conditional portfolio kurtosis largely aligns with narratives on financial stress as a result of the global financial crisis in 2008, the European sovereign debt crisis (2010-2013) and early rumors signalling the United Kingdom to leave the European Union (2017). These insights are consistent with a recently proposed model that associates portfolio kurtosis with a geopolitical risk factor.

Automated summary

This study introduces a model for dynamic independent component analysis, where the dynamics are driven by the score of the pseudo likelihood with respect to the rotation angle of model innovations. The simulation study shows good finite sample properties of the estimator. In an application to exchange rate series, the model-implied conditional portfolio kurtosis aligns with narratives on financial stress, consistent with a recently proposed model.