Value-at-risk forecasting with combined neural network model
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.