Title :
Value-at-risk forecasting with combined neural network model
Author :
Lu Huapu ; Yu Xinxin ; Zhu Jianan ; Zhao Xiaoqiang ; Cheng Nan
Author_Institution :
Inst. of Transp. Eng., Tsinghua Univ., Beijing, China
Abstract :
This paper develops a neural network model for solving the Value-at-risk forecasting problems. The application of forecasting methods in neural network models is discussed, which involves normal-GARCH model and grey forecasting model. Compared to the use of traditional models, the new method is fast, easy to implement, numerically reliable. After describing the model, experimental results from Chinese equity market verify the effectiveness and applicability of the proposed work.
Keywords :
forecasting theory; grey systems; neural nets; Chinese equity market; combined neural network model; grey forecasting model; normal-GARCH model; value-at-risk forecasting; Artificial neural networks; Computational modeling; Forecasting; Indexes; Mathematical model; Numerical models; Predictive models; GARCH model; Grey forecasting model; Value-at-risk; neural network;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583173