A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market ReturnsCitation formats

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A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns. / Becker, Ralf; O'Neill, Robert; Clements, Adam.

In: Econometrics, Vol. 6, No. 1, 2018.

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Becker, Ralf ; O'Neill, Robert ; Clements, Adam. / A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns. In: Econometrics. 2018 ; Vol. 6, No. 1.

Bibtex

@article{4bd7a1f724c54bf3af5010f445e19fe2,
title = "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns",
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.",
keywords = "volatility forecasting, similarity forecasting, kernel density estimation",
author = "Ralf Becker and Robert O'Neill and Adam Clements",
year = "2018",
doi = "10.3390/econometrics6010007",
language = "English",
volume = "6",
journal = "Econometrics",
issn = "2225-1146",
publisher = "M D P I AG",
number = "1",

}

RIS

TY - JOUR

T1 - A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns

AU - Becker, Ralf

AU - O'Neill, Robert

AU - Clements, Adam

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - volatility forecasting

KW - similarity forecasting

KW - kernel density estimation

U2 - 10.3390/econometrics6010007

DO - 10.3390/econometrics6010007

M3 - Article

VL - 6

JO - Econometrics

JF - Econometrics

SN - 2225-1146

IS - 1

ER -