Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?

Yongsheng Yi, Mengxi He and Yaojie Zhang

The North American Journal of Economics and Finance, 2022, vol. 62, issue C

Abstract: Inspired by cross-market information flows among international stock markets, we incorporate external predictive information from other cryptocurrency markets to forecast the realized volatility (RV) of Bitcoin. To make the most of such external information, we employ six widely accepted approaches to construct predictive models based on multivariate information. Our results suggest that the scaled principal component analysis (SPCA) approach steadily improves the predictive ability of the prevailing heterogeneous autoregressive (HAR) benchmark model considering both the model confidence set (MCS) test and the Diebold–Mariano (DM) test based on three widely accepted loss functions. The forecasting performance is persistent to various robustness checks and extensions. Notably, a mean–variance investor can obtain steady positive economic gains if the investment portfolio is constructed on the basis of the forecasts from the HAR-SPCA model. The results of this study show that external predictive information is statistically and economically important in forecasting Bitcoin RV.

Keywords: Bitcoin; Cryptocurrency; Realized volatility; Out-of-sample prediction; Scaled principal component analysis (search for similar items in EconPapers)
JEL-codes: C22 C53 G11 G15 (search for similar items in EconPapers)
Date: 2022
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:62:y:2022:i:c:s106294082200081x

DOI: 10.1016/j.najef.2022.101731

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