Dynamic Score-Driven Independent Component Analysis
提出一种动态独立成分分析模型，其动态由模型创新旋转角的伪似然得分驱动。估计量一致且渐近正态，模拟显示有限样本性质良好。应用于欧元和英镑兑美元汇率，发现条件峰度与2008年金融危机、欧债危机及英国脱欧传闻等金融压力叙事高度一致。
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 mis-specification of the innovation density. In an application to a bivariate exchange rate series of the Euro and the British Pound against the US 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 UK to leave the European Union (2017). These insights are consistent with a recently proposed model that associates portfolio kurtosis with a geopolitical risk factor.