Improved portfolio selection using market expectations through penalization
The portfolio selection problem can be viewed as an optimization problem that maximizes a risk-return rela-tion. It has of course, an objective function, decision variables and input parameters: expected returns and covariance between them. These parameters' real values are not achievable for u...
- Autores:
-
Segura Acosta, Diego Hernán
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/34699
- Acceso en línea:
- http://hdl.handle.net/1992/34699
- Palabra clave:
- Administración del portafolio - Modelos matemáticos
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | The portfolio selection problem can be viewed as an optimization problem that maximizes a risk-return rela-tion. It has of course, an objective function, decision variables and input parameters: expected returns and covariance between them. These parameters' real values are not achievable for us, therefore, estimations are needed and they are commonly based on historical data; apart from the error that this haul into the problem, it is not all the information that we could use. Options market prices are a rich source of investors' expecta-tions according to their knowledge about options' underlyings, thus, we propose the usage of a new estimator for risk and return that mixes up historical and implicit information into the portfolio selection problem. We implemented the new estimators for the Mean-VAR and Mean-VaR2 problems using an elastic-net model that helps us lessening the risk of all the estimations made. |
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