| Autor: |
Juan D. Velásquez, Sarah Gutiérrez, Carlos J. Franco |
| Jazyk: |
English<br />Spanish; Castilian<br />Portuguese |
| Rok vydání: |
2013 |
| Předmět: |
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| Zdroj: |
Revista Ingenierías Universidad de Medellín, Vol 12, Iss 22, Pp 127-136 (2013) |
| Druh dokumentu: |
article |
| ISSN: |
1692-3324 |
| Popis: |
The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptron and an ARCH model to predict the monthly conditional variance of stock prices. The results show that DAN2 model is more accurate for predicting in-sample and out-of-sample variance that the other considered models for the used dataset. Thus, the value of this neural network as a predictive tool is demonstrated. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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