Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model

Zhenlong Chen, Junjie Liu and Xiaozhen Hao

Finance Research Letters, 2024, vol. 67, issue PA

Abstract: In recent years, cryptocurrencies have gained investor attention for their extreme volatility, but this has introduced financial risks that require accurate prediction models. Therefore, we propose the SHARV-MGJR model, which incorporates both ‘good-bad’ volatility, leverage effects, and current return information to enhance the accuracy of cryptocurrency market volatility predictions. Empirical results demonstrate that compared to GARCH-type models, the SHARV-MGJR model exhibits superior predictive accuracy in forecasting cryptocurrency market volatility. Furthermore, robustness tests confirm the superiority of the SHARV-MGJR model in predicting cryptocurrency market volatility.

Keywords: SHARV-MGJR model; Volatility forecasting; Cryptocurrency market; Leverage effect; Current return information; ‘Good-bad’ volatility (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 C58 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:67:y:2024:i:pa:s1544612324007876

DOI: 10.1016/j.frl.2024.105757

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