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| Hoe, Lam Weng; Saiful Hafizah, Jaaman; Zaidi, Isa. |
| Risk is one of the important parameters in portfolio optimization problem. Since the introduction of the mean-variance model, variance has become the most common risk measure used by practitioners and researchers in portfolio optimization. However, the mean-variance model relies strictly on the assumptions that assets returns are multivariate normally distributed or investors have a quadratic utility function. Many studies have proposed different risk measures to overcome the drawbacks of variance. The purpose of this paper is to discuss and compare the portfolio compositions and performances of four different portfolio optimization models employing different risk measures, specifically the variance, absolute deviation, minimax and semi-variance. Results... |
| Tipo: Journal Article |
Palavras-chave: Portfolio; Optimization; Risk measures; Variance.; Financial Economics; CO2; C61; G11. |
| Ano: 2010 |
URL: http://purl.umn.edu/95934 |
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| Ribeiro,Marta Jeidjane Borges; Pinto,Luís Fernando Batista; Barbosa,Ana Carla Borges; Santos,Gladston Rafael de Arruda; Pinto,Ana Paula Gomes; Nascimento,Carlos Souza do; Barbosa,Leandro Teixeira. |
| ABSTRACT: This study aimed to identify the principal components (PC) that explain the highest percentages of total variance and best characterize the in vivo and carcass morphologies of Anglo-Nubian crossbred goats. Nineteen carcass morphometric traits and six in vivo morphometric traits were measured in 28 kids at eight months of age. Principal component analysis indicated that five PC were able to explain 83.57% of the total variance in the 19 original carcass traits. Those components were termed PC1-Carcass Size, PC2 - Body Condition, PC3-Carcass Width, PC4-Chest Depth, and PC5 - Hindquarter. For in vivo morphometric traits, the first two principal components explained 78.86% of the total variance. These components were called PC1-In vivo Size and... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Carcass; Correlation; Multivariate analysis; Variance.. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782018000600653 |
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