Cryptocurrency uncertainty and volatility forecasting of precious metal futures markets

被引:20
|
作者
Wei, Yu [1 ]
Wang, Yizhi [2 ]
Lucey, Brian M. [3 ,4 ,5 ,6 ]
Vigne, Samuel A. [7 ]
机构
[1] Yunnan Univ Finance & Econ, Sch Finance, Kunming, Peoples R China
[2] Cardiff Univ, Cardiff Business Sch, Aberconway Bldg, Cardiff CF10 3EU, Wales
[3] Trinity Coll Dublin, Trinity Business Sch, Dublin, Ireland
[4] Univ Econ Ho Chi Minh City, Inst Business Res, 59C Nguyen Dinh Chieu, Ward 6, Dist 3, Ho Chi Minh City, Vietnam
[5] Jiangxi Univ Econ & Finance, Inst Ind Econ, 169, East Shuanggang Rd, Nanchang 330013, Jiangxi, Peoples R China
[6] Univ Abu Dhabi, Abu Dhabi, U Arab Emirates
[7] LUISS Univ, LUISS Business Sch, Rome, Italy
基金
中国国家自然科学基金;
关键词
Cryptocurrency uncertainty; Precious metal; Volatility forecasting; Model evaluation; GOLD; COMBINATION; BITCOIN;
D O I
10.1016/j.jcomm.2022.100305
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Several common properties shared by cryptocurrencies and precious metals, such as safe haven, hedge and diversification for risk assets, have been widely discussed since Bitcoin was created in 2008. However, no studies have explored whether cryptocurrency market uncertainties can help to explain and forecast volatilities in precious metal markets. By using the GARCH-MIDAS model incorporating cryptocurrency policy and price uncertainty, as well as several other commonly used uncertainty measures, this paper compares the in-sample impacts and out-of -sample predictive abilities of these uncertainties on volatility forecasts of COMEX gold and silver futures markets. The in-sample results demonstrate the significant impacts of cryptocurrency uncertainty on the volatilities of precious metal futures markets, and the out-of-sample evidence further confirms the superior predictive power of cryptocurrency uncertainty on volatility forecasting of the precious metal market. Our conclusions are robust through various model evaluation approaches based not only on predicting errors but also on forecasting directions across different forecasting time horizons.
引用
收藏
页数:16
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