Title :
Volatility estimation with a neural network
Author :
Freisleben, Bernd ; Ripper, Klaus
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Siegen Univ., Germany
Abstract :
The prediction of the volatility of financial time-series is very important for the evaluation and pricing of options and the development of option trading strategies. In this paper, a neural network for predicting the volatility of the German Bund future is presented. Its performance is compared to that of a nonlinear GARCH model
Keywords :
costing; economic cybernetics; electronic trading; financial data processing; neural nets; performance evaluation; probability; stock markets; time series; German Bund future; financial time-series volatility estimation; neural network; nonlinear GARCH model; option pricing; option trading strategies; performance; probability; Analysis of variance; Computer science; Data analysis; Econometrics; Fading; Gaussian distribution; Integrated circuit modeling; Neural networks; Pricing; Product development;
Conference_Titel :
Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-4133-3
DOI :
10.1109/CIFER.1997.618932
