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• W3048157393 abstract "A new method is proposed for the analysis of multivariate stochastic volatility models, based on efficient draws of volatility from its conditional posterior distribution. It applies to models with several kinds of cross-sectional dependence. Full autoregression and covariance matrices imply dependent volatility series. Mean factor structure allows conditional correlations to vary in time and covary with conditional variances; factors are conditionally Student’s t, allowing for tail dependence across assets, with factor-specific degrees of freedom. Given factors, returns have heterogeneous Student’s t marginals; a copula completes their joint distribution. Volatility series are drawn as a block, one series at a time. An application using daily returns data for ten currencies shows that all features of the model are important." @default.
• W3048157393 created "2020-08-13" @default.
• W3048157393 creator A5025475323 @default.
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• W3048157393 date "2021-01-01" @default.
• W3048157393 modified "2023-09-27" @default.
• W3048157393 title "Multivariate stochastic volatility using the HESSIAN method" @default.
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• W3048157393 doi "https://doi.org/10.1016/j.ecosta.2020.07.002" @default.
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