Модели волатильности, основанные на нечётких системах, с применением к российскому фондовому рынку

Проблемы управления. 2022. № 6. С. 26–34.

Свиязов В. А.

Volatility modeling and forecasting is a topical problem both in scientific circles and in the practice. This paper develops an approach combining the GARCH model and fuzzy logic. The Takagi–Sugeno fuzzy inference scheme is adopted to fuzzify an original autoregression model (the conditional heteroskedasticity model). As a result, several different local GARCH models can be used in different input data domains with soft switching between them. This ap- proach allows considering such phenomena as volatility clustering and asymmetric volatility (the properties of real financial markets). The proposed algorithm is applied to the historical values of the RTS Index and compared with the classical GARCH model. As demonstrated be- low, in several cases, fuzzy models have advantages over traditional ones, namely, higher fore- casting accuracy. Thus, the proposed method should be considered among others when model- ing the volatility of the Russian financial market instruments: it demonstrates qualities superior to the conventional counterparts.

Keywords: волатильность, временные ряды, прогнозирование, нечёткие системы