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• W1639914946 abstract "This paper proposes a new method for forecasting covariance matrices of financial returns. the model mixes volatility forecasts from a dynamic model of daily realized volatilities estimated with high-frequency data with correlation forecasts based on daily data. This new approach allows for flexible dependence patterns for volatilities and correlations, and can be applied to covariance matrices of large dimensions. The seperate modeling of volatility and correlation forecasts considerably reduces the estimation and measurement error implied by the joint estimation and modeling of covariance matrix dynamics. Our empirical results show that the new mixing approach provides superior forecasts compared to multivariate volatility specifications using single sources of information." @default.
• W1639914946 created "2016-06-24" @default.
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• W1639914946 date "2011-01-01" @default.
• W1639914946 modified "2023-09-24" @default.
• W1639914946 title "Forecasting Covariance Matrices: A Mixed Frequency Approach" @default.
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• W1639914946 doi "https://doi.org/10.2139/ssrn.1740587" @default.
• W1639914946 hasPublicationYear "2011" @default.
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