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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Distrital Francisco José de Caldas
2020
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| Online-Zugang: | https://www.redalyc.org/articulo.oa?id=504373006002 |
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Inhaltsangabe:
- Estimating Market Expectations for Portfolio Selection Using Penalized Statistical Models Carlos Felipe Valencia-Arboleda Diego Hernán Segura-Acosta Multidisciplinarias (Ciencias Sociales) regularization Penalized models financial options portfolio optimization state price density estimation The portfolio selection problem can be viewed as an optimization problem that maximizes the risk–return relationship. It consists of a number of elements, such as an objective function, decision variables and input parameters, which are used to predict expected returns and the covariance between the said returns. However, the real values of these parameters cannot be directly observed; thus, estimations based on historical data are required. Historical data, however, can often result in modelling errors when the parameters are replaced by their estimations. We propose to address this by using some regularization mechanisms in the optimization. In addition, we explore the use of implicit information to improve the portfolio performance, such as options market prices, which are a rich source of investor expectations. Accordingly, we propose a new estimator for risk and return that combines historical and implicit information in the portfolio selection problem. We implement the new estimators for the mean-VAR and mean-VaR2 problems using an elastic-net model that reduces the risk of all estimations performed. The results suggest that the model has a good out-ofsample performance that is superior to models with pure historical estimations. 2020 artículo científico 0124-2253 https://www.redalyc.org/articulo.oa?id=504373006002 en http://www.redalyc.org/revista.oa?id=5043 Revista Científica application/pdf Universidad Distrital Francisco José de Caldas Revista Científica (Colombia) Num.2 Vol.38