Abstract
Volatility forecasting in the financial markets, along with the development of financial models, is important in the areas of risk management and asset pricing, among others. Previous testing has shown that asymmetric GARCH models outperform other GARCH family models with regard to volatility prediction. Utilizing this information, three popular Neural Network models (Feed-Forward with Back Propagation, Generalized Regression, and Radial Basis Function) are implemented to help improve the performance of the GJR (1,1) method for estimating volatility over the next forty-four trading days. During training and testing, four different economic cycles have been considered between 1997-2011 to represent real and contemporary periods of market calm and crisis. In addition to stress testing for different neural network architectures to assess their performance under various turmoil and normal situations in the U.S. market, their synergy along with another econometric model is also accessed.
Recommended Citation
S. A. Monfared and D. L. Enke, "Volatility Forecasting using a Hybrid GJR-GARCH Neural Network Model," Procedia Computer Science, vol. 36, pp. 246 - 253, Elsevier, Jan 2014.
The definitive version is available at https://doi.org/10.1016/j.procs.2014.09.087
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Asymmetric GARCH; Radial basis functions; Stress testing; Volatility forecasting
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Jan 2014
