Estimating returns and condicional volatility: a comparison between the ARMA-GARCH-M Models and the Backpropagation Neural Network
Econometric models have usually estimated both returns and conditional volatility in financial assets. This paper is intended in the comparison of this traditional approach with the more recent Backpropagation neural network. When applied to the Spanish Ibex-35 stock market index, we find that the n...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/60000 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/60000 |
| Access Level: | acceso abierto |
| Palabra clave: | Conditional volatility Backpropagation neural network GARCH-M ECONOMIA FINANCIERA Y CONTABILIDAD |
| Sumario: | Econometric models have usually estimated both returns and conditional volatility in financial assets. This paper is intended in the comparison of this traditional approach with the more recent Backpropagation neural network. When applied to the Spanish Ibex-35 stock market index, we find that the neural network achieved significantly better performance in predicting conditional volatility, but similar results when predicting financial returns. |
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