Abstract

VELASQUEZ, Juan D.; GUTIERREZ, Sarah  and  FRANCO, Carlos J.. USING A DYNAMIC ARTIFICIAL NEURAL NETWORK FOR FORECASTING THE VOLATILITY OF A FINANCIAL TIME SERIES. Rev. ing. univ. Medellín [online]. 2013, vol.12, n.22, pp.127-136. ISSN 1692-3324.

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.

Keywords : Volatility forecast; prediction; nonlinear models; heteroskedasticity.

        · abstract in Spanish     · text in English     · English ( pdf )