Dynamic score driven independent component analysis

Christian Hafner and Helmut Herwartz

No 2022010, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: 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 seriesof 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 arecently proposed model that associates portfolio kurtosis with a geopolitical risk factor.

Keywords: Structural vector autoregressions; multivariate GARCH; portfolio selection; risk management (search for similar items in EconPapers)
Pages: 61
Date: 2022-02-01
Note: In: Journal of Business & Economic Statistics, 2022
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Working Paper: Dynamic score driven independent component analysis (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2022010

DOI: 10.1080/07350015.2021.2013244

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