A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
| Autor: | Robert O’Neill, Adam Clements, Ralf Becker |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Economics and Econometrics
Multivariate statistics 050208 finance lcsh:HB71-74 Computer science 05 social sciences Kernel density estimation volatility forecasting MathematicsofComputing_NUMERICALANALYSIS lcsh:Economics as a science kernel density estimation similarity forecasting Covariance Matrix (mathematics) 0502 economics and business Econometrics ddc:330 Stock market C58 050207 economics C53 Stock (geology) Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Econometrics; Volume 6; Issue 1; Pages: 7 Becker, R, O'Neill, R & Clements, A 2018, ' A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns ', Econometrics, vol. 6, no. 1, 7 . https://doi.org/10.3390/econometrics6010007 Econometrics, Vol 6, Iss 1, p 7 (2018) |
| ISSN: | 2225-1146 |
| DOI: | 10.3390/econometrics6010007 |
| Popis: | 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. © 2018 by the authors. |
| Databáze: | OpenAIRE |
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