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• W2790669571 abstract "This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. The model makes use of similarity forecasting techniques and it is demonstrated that several popular techniques can be thought as a subset of this approach. A forecasting experiment demonstrates the potential for the technique to improve the statistical accuracy of forecasts of variance-covariance matrices." @default.
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• W2790669571 date "2018-02-17" @default.
• W2790669571 modified "2023-09-27" @default.
• W2790669571 title "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns" @default.
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• W2790669571 doi "https://doi.org/10.3390/econometrics6010007" @default.
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