Applying nonlinear generalized autoregressive conditional heteroscedasticity to compensate ANFIS outputs tuned by adaptive support vector regression

Volatility clustering degrades the efficiency and effectiveness of time series prediction and gives rise to large residual errors. This is because volatility clustering suggests a time series where successive disturbances, even if uncorrelated, are yet serially dependent. To overcome volatility clus...

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Bibliographic Details
Published in:Fuzzy sets and systems 2006-07, Vol.157 (13), p.1832-1850
Main Author: Rong Chang, Bao
Format: Article
Language:English
Subjects:
Adaptive support vector regression
ANFIS/NGARCH composite model
Applied sciences
Circuit properties
Computer science
control theory
systems
Control theory. Systems
Digital circuits
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Heteroscedasticity
Information, signal and communications theory
Mathematical methods
Miscellaneous
System theory
Telecommunications and information theory
Theoretical computing
Time series prediction
Volatility clustering
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Summary:Volatility clustering degrades the efficiency and effectiveness of time series prediction and gives rise to large residual errors. This is because volatility clustering suggests a time series where successive disturbances, even if uncorrelated, are yet serially dependent. To overcome volatility clustering problems, an adaptive neuro-fuzzy inference system (ANFIS) is combined with a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model that is tuned by adaptive support vector regression (ASVR) so as to tackle the problem of time-varying conditional variance in residual errors. The proposed method significantly reduces large residual errors in forecasts because volatility clustering effects are regulated to trivial levels. Two experiments using real financial data series compare the proposed method and a number of well-known alternative methods. Results show that forecasting performance by the proposed method produces superior results, with good speed of computation. Goodness of fit of the proposed method is tested by Ljung–Box Q-test. It is concluded that the ANFIS/NGARCH composite model tuned by ASVR performs very well for improved predictive accuracy of irregular non-periodic short-term time series forecast and will be of interest to the science of statistical prediction of time series.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2006.01.011