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...

Full description

Autores:

Velásquez, Juan D.
Gutiérrez, Sarah
Franco, Carlos J.

Tipo de recurso:

Article of journal

Fecha de publicación:

2013

Institución:

Universidad de Medellín

Repositorio:

Repositorio UDEM

Idioma:

spa

OAI Identifier:

oai:repository.udem.edu.co:11407/962

Acceso en línea:

http://hdl.handle.net/11407/962

Palabra clave:

Volatility forecast
prediction
nonlinear models
heteroskedasticity
volatilidad (finanzas)
modelos no lineales
heterocedasticidad

Rights

License

http://creativecommons.org/licenses/by-nc-sa/4.0/