Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
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 perceptronand an ARCH model to predict the monthly conditional variance of stock prices.The results show that DAN2 model is more accurate for predic...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Colombia |
| Institución: | Universidad de Medellín |
| Repositorio: | Repositorio UDEM |
| Idioma: | español |
| OAI Identifier: | oai:repository.udem.edu.co:11407/962 |
| Acceso en línea: | http://hdl.handle.net/11407/962 |
| Access Level: | acceso abierto |
| Palabra clave: | Volatility forecast prediction nonlinear models heteroskedasticity volatilidad (finanzas) modelos no lineales heterocedasticidad |
| Sumario: | 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 perceptronand 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 andout-of-sample variance that the other considered models for the used data set. Thus, the value of this neural network as a predictive tool is demonstrated. |
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