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Can Twitter-based economic uncertainty predict safe-haven assets under all market conditions and investment horizons?

机译:基于Twitter的经济不确定性能否预测所有市场条件和投资期限下的避险资产?

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摘要

This paper examines the Granger causality from Twitter-based economic uncertainty (TEU) to three safe-haven assets - Bitcoin, gold, and US10 year Treasury notes. Using daily data (June 1, 2011-August 30, 2021) and causality-in-quantiles and wavelets methods, the results indicate variability in the causality between the mean and variance, as well as the market conditions. TEU Granger-causes the returns and volatility of Treasuries, the volatility but not returns of Bitcoin, and neither the volatility nor the returns of gold for the raw series, and the causality is mostly significant at low and middle quantiles for Bitcoin and Treasuries. We include other risk factors and confirm the variability in the causality. Considering the possibility of a hidden causality over various frequency domains due to investors' heterogeneous expectations and perceptions of risk, the wavelet transforms -based causality tests reveal an increase in the predictability of risk indicators under specific investment horizons and market conditions. During the pandemic, TEU strongly predicts future volatility of Treasury and Bitcoin returns, reflecting the importance of social-media posts for safe-haven pricing. These findings highlight the benefits of applying the causality-in-quantiles test to decomposed series to determine the contribution of each scale to the causality over various market conditions.
机译:本文研究了基于Twitter的经济不确定性(TEU)与三种避险资产(比特币、黄金和美国10年期国债)的格兰杰因果关系。使用每日数据(2011 年 6 月 1 日至 2021 年 8 月 30 日)以及因果分位数和小波方法,结果表明均值和方差之间的因果关系以及市场条件存在变异性。TEU Granger-导致美国国债的收益和波动性,比特币的波动性但不引起收益,黄金的波动性和非收益都不是原始系列的波动性和回报,并且因果关系在比特币和国债的中低分位数上最显著。我们纳入了其他危险因素,并确认了因果关系的变异性。考虑到投资者对风险的异质性预期和感知导致的各频域上隐藏因果关系的可能性,基于小波变换的因果关系检验揭示了在特定投资期限和市场条件下风险指标的可预测性增加。在大流行期间,TEU强烈预测美国国债和比特币回报的未来波动性,反映了社交媒体帖子对避险定价的重要性。这些发现强调了将分量因果关系检验应用于分解序列的好处,以确定每个量表对各种市场条件下因果关系的贡献。

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