媒体关注与情绪作为条件波动预测的驱动因素:英国退欧的应用

Massimo Guidolin, Manuela Pedio
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引用次数: 6

摘要

使用关于英国脱欧公投的国际、在线媒体报道和语气的数据,我们测试了在预测富时100指数每周股票收益的条件方差时,是媒体报道还是语气提供了最大的预测性能改进。我们发现标准对称和非对称广义自回归条件异方差(GARCH)模型的扩展版本包括媒体覆盖率,特别是媒体语气评分,在样本内和样本外都优于传统GARCH模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit
Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models augmented to include media coverage and especially media tone scores outperform traditional GARCH models both in- and out-of-sample.
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