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- W4296472007 abstract "Analysis of multivariate time series is a common problem in areas like finance and economics. The classical tool for this purpose are vector autoregressive models. These however are limited to the modeling of linear and symmetric dependence. We propose a novel copula-based model which allows for non-linear and asymmetric modeling of serial as well as between-series dependencies. The model exploits the flexibility of vine copulas which are built up by bivariate copulas only. We describe statistical inference techniques for the new model and demonstrate its usefulness in three relevant applications: We analyze time series of macroeconomic indicators, of electricity load demands and of bond portfolio returns." @default.
- W4296472007 created "2022-09-21" @default.
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- W4296472007 date "2012-03-15" @default.
- W4296472007 modified "2023-09-29" @default.
- W4296472007 title "COPAR - Multivariate time series modeling using the COPula AutoRegressive model" @default.
- W4296472007 doi "https://doi.org/10.48550/arxiv.1203.3328" @default.
- W4296472007 hasPublicationYear "2012" @default.
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