Adaptive fuzzy system to forecast financial time series volatility

Authors:
Ivette Luna;Rosangela Ballini
Affiliations:
Department of Economic Theory of the Institute of Economics, University of Campinas-SP, Campinas, São Paulo, Brazil;Department of Economic Theory of the Institute of Economics, University of Campinas-SP, Campinas, São Paulo, Brazil
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2012

Citing 10
Cited 0

Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
A fast learning algorithm for parismonious fuzzy neural systems

Fuzzy Sets and Systems - Information processing
Adaptive mixtures of local experts

Neural Computation
Evolving fuzzy modelling in risk analysis

International Journal of Intelligent Systems in Accounting and Finance Management - Risk Analysis in Complex Systems: Intelligent Systems in Finance
Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction

Fuzzy Sets and Systems
An approach to online identification of Takagi-Sugeno fuzzy models

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

IEEE Transactions on Fuzzy Systems
Structurally adaptive modular networks for nonstationary environments

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.