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.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.