Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195444
Authors: 
Becker, Ralf
Clements, Adam
O'Neill, Robert
Year of Publication: 
2018
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 6 [Year:] 2018 [Issue:] 1 [Pages:] 1-27
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.
Subjects: 
volatility forecasting
kernel density estimation
similarity forecasting
JEL: 
C53
C58
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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