Forecasting Financial Market Volatility Using a Dynamic Topic Model

被引：17
|
作者：
Morimoto T. [1 ]
Kawasaki Y. [2 ]
机构：
[1] Department of Mathematical Sciences, Kwansei Gakuin University, 2-1 Gakuen, Sanda, 6691337, Hyogo
[2] Department of Statistical Modeling, The Institute of Statistical Mathematics and SOKENDAI, 10-3 Midori-cho, Tachikawa, 1908562, Tokyo
来源：
Asia-Pacific Financial Markets | 2017年 / 24卷 / 3期
基金：
日本学术振兴会;
关键词：
Big data; Dynamic topic model; Forecasting; Online news; Realized volatility; Topic score;
D O I：
10.1007/s10690-017-9228-z
中图分类号：
学科分类号：
摘要：
This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy. © 2017, Springer Japan KK.
引用
收藏
页码：149 / 167
页数：18
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