Value-at-risk forecasting with combined neural network model

Conference Paper · August 2010with3 Reads
DOI: 10.1109/ICNC.2010.5583173 · Source: DBLP
Conference: Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010
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.
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    Full-text · Conference Paper · Mar 2012 · Journal of Intelligent and Fuzzy Systems