Forecasting volatility with empirical similarity and Google Trends
Forecasting volatility with empirical similarity and Google Trends
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DOI：
10.1016/j.jebo.2015.06.005
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发表时间：
2015-09-01
影响因子：
2.2
通讯作者：
Heiden, Moritz
中科院分区：
文献类型：
--
作者：
Hamid, Alain;Heiden, Moritz
本文提出了一种经验相似方法，通过使用搜索引擎数据作为投资者对股市指数关注度的衡量标准来预测每周波动性。我们的模型对于投资者关注的基本过程没有假设，并且在样本外预测框架中显着优于传统的时间序列模型。我们发现，尤其是在高波动性的市场阶段，预测准确性随着投资者的关注而提高。对风险管理的实际影响在风险价值预测练习中得到了强调，我们的模型产生了明显更准确的预测，同时由于过度预测减少而需要更少的资本。 (C) 2015 Elsevier B.V. 保留所有权利。
This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms conventional time-series models in an out-of-sample forecasting framework. We find that especially in high-volatility market phases prediction accuracy increases together with investor attention. The practical implications for risk management are highlighted in a Value-at-Risk forecasting exercise, where our model produces significantly more accurate forecasts while requiring less capital due to fewer overpredictions. (C) 2015 Elsevier B.V. All rights reserved.