Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

Xinyu Wu, Xuebao Yin, Zaghum Umar and Najaf Iqbal

The North American Journal of Economics and Finance, 2023, vol. 67, issue C

Abstract: This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.

Keywords: Bitcoin; Price range; Volatility spillover; Crude oil; Leverage effect; Conditional Auto Regressive Range (CARR) (search for similar items in EconPapers)
JEL-codes: C53 E47 G11 G15 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:67:y:2023:i:c:s1062940823000712

DOI: 10.1016/j.najef.2023.101948

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