Value-at-risk forecasting with combined neural network model
DOI: 10.1109/ICNC.2010.5583173 Conference: Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010
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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ABSTRACT: This paper introduces an adaptive fuzzy rule-based system applied as a financial time series model for volatility forecasting. The model is based on Takagi–Sugeno fuzzy systems and is built in two phases: In the first, the model uses the subtractive clustering algorithm to determine initial group structures in a reduced data set. In the second phase, the system is modified dynamically by adding and pruning operators and applying a recursive learning algorithm based on the expectation maximization optimization technique. The algorithm automatically determines the number of fuzzy rules necessary at each step, and one-step-ahead predictions are estimated and parameters updated. The model is applied to forecast financial time series volatility, considering daily values of the São Paulo stock exchange index, the Petrobras preferred stock prices, and the BRL/USD exchange rate. The model suggested is compared against generalized autoregressive conditional heteroskedasticity models. Experimental results show the adequacy of the adaptive fuzzy approach for volatility forecasting purposes.Journal of Intelligent and Fuzzy Systems 01/2012; 23(1):27-38. DOI:10.3233/IFS-2012-0491 · 1.81 Impact Factor
Conference Paper: Online Estimation of Stochastic Volatility for Asset Returns[Show abstract] [Hide abstract]
ABSTRACT: This paper suggests an adaptive fuzzy rule based system applied as a financial time series model for volatility forecasting. The model is based on Takagi-Sugeno fuzzy systems, and it is built in two phases. In the first phase, the model uses the Subtractive Clustering algorithm to determine group structures in a reduced data set for initialization purpose. In the second phase, the system is modified dynamically via adding and pruning operators and a recursive learning algorithm, which is based on the Expectation Maximization optimization technique. The online algorithm determines automatically the number of fuzzy rules necessary at each step, whereas one step ahead predictions are estimated and parameters are updated as well. The model is applied for forecasting financial time series volatility, considering daily values the REAL/USD exchange rate. The model suggested is compared against generalized autoregressive conditional heteroskedaticity models. Experimental results show the adequacy of the adaptative fuzzy approach for volatility forecasting purposes.IEEE Computational Intelligence for Financial Engineering and Economics, New York; 03/2012
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