a multivariate kernel approach to forecasting the variance covariance of stock market returns

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ID: 161850
2018
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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.
Reference Key
becker2018econometricsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ralf Becker;Adam Clements;Robert O'Neill
Journal developmental cognitive neuroscience
Year 2018
DOI
10.3390/econometrics6010007
URL
http://www.mdpi.com/2225-1146/6/1/7
Keywords
kernel density estimation similarity forecastingeconomics as a science

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