DocumentCode
1991110
Title
Modeling volatility of time series using fuzzy GARCH models
Author
Popov, A.A. ; Bykhanov, K.V.
Author_Institution
Novosibirsk State Tech. Univ., Russia
fYear
2005
fDate
26 June-2 July 2005
Firstpage
687
Lastpage
692
Abstract
Fuzzy modeling is an effective way of construction models for complex dynamic systems. Here we present a new application of fuzzy rule-based models to analysis of discrete time series. Fuzzy generalization of autoregressive conditional heteroscedasticity (ARCH/GARCH) models is proposed and technology of fuzzy GARCH modeling is basically overviewed. A comparison with usual GARCH models is made both for modeled and real time series.
Keywords
autoregressive processes; economics; finance; fuzzy set theory; generalisation (artificial intelligence); risk analysis; time series; autoregressive conditional heteroscedasticity; complex dynamic systems; discrete time series; fuzzy GARCH models; fuzzy generalization; fuzzy modeling; fuzzy rule-based models; volatility modeling; Analysis of variance; Equations; Fuzzy systems; Mathematical model; Nonlinear dynamical systems; Random processes; Takagi-Sugeno model; Testing; Time series analysis; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Science and Technology, 2005. KORUS 2005. Proceedings. The 9th Russian-Korean International Symposium on
Print_ISBN
0-7803-8943-3
Type
conf
DOI
10.1109/KORUS.2005.1507875
Filename
1507875
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