Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
DOI:
https://doi.org/10.22395/rium.v12n22a11Keywords:
Volatility forecast, prediction, nonlinear models, heteroskedasticity, volatilidad (finanzas), modelos no lineales, heterocedasticidadAbstract
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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