Neural network volatility forecasts

José R. Aragonés, Carlos Blanco and Pablo García Estévez

Intelligent Systems in Accounting, Finance and Management, 2007, vol. 15, issue 3‐4, 107-121

Abstract: We analyse whether the use of neural networks can improve ‘traditional’ volatility forecasts from time‐series models, as well as implied volatilities obtained from options on futures on the Spanish stock market index, the IBEX‐35. One of our main contributions is to explore the predictive ability of neural networks that incorporate both implied volatility information and historical time‐series information. Our results show that the general regression neural network forecasts improve the information content of implied volatilities and enhance the predictive ability of the models. Our analysis is also consistent with the results from prior research studies showing that implied volatility is an unbiased forecast of future volatility and that time‐series models have lower explanatory power than implied volatility. Copyright © 2008 John Wiley & Sons, Ltd.

Date: 2007
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