FORECASTING BITCOIN VOLATILITY USING TWO-COMPONENT CARR MODEL

【Author】 Wu, Xinyu; Niu, Shenghao; Xie, Haibin

【Source】ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH

【影响因子】0.899

【Abstract】In this paper, we propose an extension of the range-based CARR model, the two-component CARR (CCARR) model to model and forecast the Bitcoin volatility. The extension inherits the strength of the original range-based CARR model, its capability of exploiting intraday information from the high and low prices to estimate volatility. Moreover, the CCARR model has the capacity to accommodate the long memory volatility. Empirical results show that the CCARR model outperforms the CARR model and the return-based GARCH and two-component GARCH (CGARCH) models in forecasting the Bitcoin volatility. The results highlight the value of using price range and including a second component of the conditional range for forecasting the Bitcoin volatility.

【Keywords】Bitcoin; Two-component CARR; Price range; Two-component GARCH; Volatility forecasting; Long memory

【发表时间】2020

【收录时间】2022-01-02

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