Comparison among High Dimensional Covariance Matrix Estimation Methods
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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Nacional de Colombia
2011
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| _version_ | 1876451303559790592 |
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| author | Karoll Gómez |
| author_facet | Karoll Gómez |
| contents | Comparison among High Dimensional Covariance Matrix Estimation Methods Karoll Gómez Santiago Gallón Física, Astronomía y Matemáticas Shrinkage Covariance matrix High dimensional data Portfolio optimization Penalized least squares Accurate measures of the volatility matrix and its inverse play a central role in risk and portfolio management problems. Due to the accumulation of errors in the estimation of expected returns and covariance matrix, the solution to these problems is very sensitive, particularly when the number of assets (p) exceeds the sample size (T ). Recent research has focused on developing different methods to estimate high dimensional covariance matrixes under small sample size. The aim of this paper is to examine and compare the minimum variance optimal portfolio constructed using five different estimation methods for the covariance matrix: the sample covariance, Risk- Metrics, factor model, shrinkage and mixed frequency factor model. Using the Monte Carlo simulation we provide evidence that the mixed frequency factor model and the factor model provide a high accuracy when there are portfolios with p closer or larger than T. 2011 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89922501010 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.3 Vol.34 |
| format | Artículo científico |
| id | redalyc_89922501010 |
| institution | Redalyc |
| language | en |
| publishDate | 2011 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Comparison among High Dimensional Covariance Matrix Estimation Methods Karoll Gómez Física, Astronomía y Matemáticas Shrinkage Covariance matrix High dimensional data Portfolio optimization Penalized least squares Comparison among High Dimensional Covariance Matrix Estimation Methods Karoll Gómez Santiago Gallón Física, Astronomía y Matemáticas Shrinkage Covariance matrix High dimensional data Portfolio optimization Penalized least squares Accurate measures of the volatility matrix and its inverse play a central role in risk and portfolio management problems. Due to the accumulation of errors in the estimation of expected returns and covariance matrix, the solution to these problems is very sensitive, particularly when the number of assets (p) exceeds the sample size (T ). Recent research has focused on developing different methods to estimate high dimensional covariance matrixes under small sample size. The aim of this paper is to examine and compare the minimum variance optimal portfolio constructed using five different estimation methods for the covariance matrix: the sample covariance, Risk- Metrics, factor model, shrinkage and mixed frequency factor model. Using the Monte Carlo simulation we provide evidence that the mixed frequency factor model and the factor model provide a high accuracy when there are portfolios with p closer or larger than T. 2011 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89922501010 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.3 Vol.34 |
| title | Comparison among High Dimensional Covariance Matrix Estimation Methods |
| topic | Física, Astronomía y Matemáticas Shrinkage Covariance matrix High dimensional data Portfolio optimization Penalized least squares |
| url | https://www.redalyc.org/articulo.oa?id=89922501010 |