Abstract
In the minimum variance model, the covariance matrix plays an important role because it measures the risk and relationship of asset returns simultaneously under the normality assumption. However, in practice, the distribution of asset returns is nonnormal and has an obvious fat-tail nature. In addition, the risk is one-sided. In this paper, the main objective is to propose a better tool to replace the covariance matrix. The covariance matrix can be decomposed into two parts: a diagonal variance matrix and a square matrix with its elements being the Pearson correlation coefficient. A substitution of the covariance matrix is presented by replacing the variance and Pearson correlation coefficient in the decomposition of the covariance matrix with a semivariance and distance correlation coefficient, respectively. The proposed portfolio optimization strategy is applied to empirical data, and the numerical studies show the strategy performs well.
| Original language | English |
|---|---|
| Pages (from-to) | 373-394 |
| Number of pages | 22 |
| Journal | Statistica Neerlandica |
| Volume | 73 |
| Issue number | 3 |
| Early online date | 21 Mar 2019 |
| DOIs | |
| Publication status | Published - Aug 2019 |
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Portfolio selection based on semivariance and distance correlation under minimum variance framework. / Sun, Ruili; Ma, Tiefeng; Liu, Shuangzhe.
In: Statistica Neerlandica, Vol. 73, No. 3, 08.2019, p. 373-394.Research output: Contribution to journal › Article
TY - JOUR
T1 - Portfolio selection based on semivariance and distance correlation under minimum variance framework
AU - Sun, Ruili
AU - Ma, Tiefeng
AU - Liu, Shuangzhe
PY - 2019/8
Y1 - 2019/8
N2 - In the minimum variance model, the covariance matrix plays an important role because it measures the risk and relationship of asset returns simultaneously under the normality assumption. However, in practice, the distribution of asset returns is nonnormal and has an obvious fat-tail nature. In addition, the risk is one-sided. In this paper, the main objective is to propose a better tool to replace the covariance matrix. The covariance matrix can be decomposed into two parts: a diagonal variance matrix and a square matrix with its elements being the Pearson correlation coefficient. A substitution of the covariance matrix is presented by replacing the variance and Pearson correlation coefficient in the decomposition of the covariance matrix with a semivariance and distance correlation coefficient, respectively. The proposed portfolio optimization strategy is applied to empirical data, and the numerical studies show the strategy performs well.
AB - In the minimum variance model, the covariance matrix plays an important role because it measures the risk and relationship of asset returns simultaneously under the normality assumption. However, in practice, the distribution of asset returns is nonnormal and has an obvious fat-tail nature. In addition, the risk is one-sided. In this paper, the main objective is to propose a better tool to replace the covariance matrix. The covariance matrix can be decomposed into two parts: a diagonal variance matrix and a square matrix with its elements being the Pearson correlation coefficient. A substitution of the covariance matrix is presented by replacing the variance and Pearson correlation coefficient in the decomposition of the covariance matrix with a semivariance and distance correlation coefficient, respectively. The proposed portfolio optimization strategy is applied to empirical data, and the numerical studies show the strategy performs well.
KW - distance correlation
KW - downside risk
KW - fat-tail
KW - portfolio optimization
KW - semivariance
UR - http://www.scopus.com/inward/record.url?scp=85068257618&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/portfolio-selection-based-semivariance-distance-correlation-under-minimum-variance-framework
U2 - 10.1111/stan.12174
DO - 10.1111/stan.12174
M3 - Article
VL - 73
SP - 373
EP - 394
JO - Statistica Neerlandica
JF - Statistica Neerlandica
SN - 0039-0402
IS - 3
ER -