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

【Author】 Yi, Yongsheng; He, Mengxi; Zhang, Yaojie

【Source】NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE

【影响因子】3.136

【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

【发表时间】2022 NOV

【收录时间】2022-07-10

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币