Title :
Modeling volatility of time series using fuzzy GARCH models
Author :
Popov, A.A. ; Bykhanov, K.V.
Author_Institution :
Novosibirsk State Tech. Univ., Russia
fDate :
26 June-2 July 2005
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;
Conference_Titel :
Science and Technology, 2005. KORUS 2005. Proceedings. The 9th Russian-Korean International Symposium on
Print_ISBN :
0-7803-8943-3
DOI :
10.1109/KORUS.2005.1507875
