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...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2012 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/60000 |
| Online Access: | https://riunet.upv.es/handle/10251/60000 |
| Access Level: | Open access |
| Keyword: | Conditional volatility Backpropagation neural network GARCH-M ECONOMIA FINANCIERA Y CONTABILIDAD |
| Summary: | 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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