Title :
Neural networks models for the prediction of stock return volatility
Author :
Catfolis, Thierry
Author_Institution :
Dept. of Chem. Eng., Katholieke Univ., Leuven, Heverlee, Belgium
Abstract :
In this paper we will focus on the use of artificial neural networks (ANNs) for the prediction of stock return volatilities. This stock return volatility is an important and unknown parameter in the Black and Scholes formulas (1973) for the calculation of the value of stock options. We will demonstrate that it is feasible to implement a NARMAX-like structure with neural networks, and to use this architecture for predicting the stock return volatility
Keywords :
autoregressive moving average processes; neural nets; stock markets; NARMAX-like structure; artificial neural networks; neural network models; stock option values; stock return volatility prediction; Artificial neural networks; Autoregressive processes; Chemical engineering; Delay lines; Expert systems; Finance; Mathematical model; Neural networks; Predictive models; Testing;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549229
