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• W3078274016 abstract "We propose a class of score-driven realized covariance models where volatilities and correlations are separately estimated. We can thus combine univariate realized volatility models with a recently introduced class of score-driven realized covariance models based on Wishart and matrix-F distributions. Compared to the latter, the proposed models remain computationally simple at high dimensions and allow for higher flexibility in parameter estimation. Through a Monte-Carlo study, we show that the two-step maximum likelihood procedure provides accurate parameter estimates in small samples. Empirically, we find that the proposed models outperform those based on joint estimation, with forecasting gains that become more significant as the cross-section dimension increases." @default.
• W3078274016 created "2020-08-24" @default.
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• W3078274016 date "2021-04-01" @default.
• W3078274016 modified "2023-10-01" @default.
• W3078274016 title "A DCC-type approach for realized covariance modeling with score-driven dynamics" @default.
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• W3078274016 doi "https://doi.org/10.1016/j.ijforecast.2020.07.006" @default.
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