Portfolio selection using the principal components GARCH model
β Scribed by Katja Specht; Wolfgang Gohout
- Publisher
- Springer US
- Year
- 2003
- Tongue
- English
- Weight
- 295 KB
- Volume
- 17
- Category
- Article
- ISSN
- 1555-4961
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π SIMILAR VOLUMES
The nonlinear transformation of the input variables that characterises the first nonlinear principal component is modelled as a linear sum of radially-symmetric kernel functions. It is shown that the parameters of the variance maximising transformation may be obtained through the minimisation of a l
In many large environmental datasets redundant variables can be discarded without the loss of extra variation. Principal components analysis can be used to select those variables that contain the most information. Using an environmental dataset consisting of 36 meteorological variables spanning 37 y
## Ε½ . Ε½ . The application of a genetic algorithm GA to the selection of principal components PCs is proposed as an efficient method to determine the optimal multivariate regression model. This stochastic method was compared with other determinis-Ε½ . tic methods such as: exhaustive search here tak