Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
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Abstract
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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How to Cite
Velásquez, J. D., Gutiérrez, S., & Franco, C. J. (2014). Using a dynamic artificial neural network for forecasting the volatility of a financial time series. Revista Ingenierías Universidad De Medellín, 12(22), 127–136. https://doi.org/10.22395/rium.v12n22a11