英文标题：
《Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual
Tool for Economists》
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作者：
Jozef Barunik and Cathy Yi-Hsuan Chen and Jan Vecer
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最新提交年份：
2019
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英文摘要：
We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.
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中文摘要：
我们提出如何利用纳斯达克新闻平台的高频新闻和支持向量机分类器量化高频市场情绪。新闻随机进入市场，由此产生的新闻情绪表现为一个随机过程。为了刻画情绪、价格和波动率的联合演化，我们引入了一个统一的连续时间情绪驱动随机波动率模型。我们提供了波动矩和新闻情绪过程的封闭式公式，并研究了新闻影响。此外，我们还实现了一种基于仿真的参数标定方法。从经验上看，我们发现，新闻情绪提高了波动性逆转的门槛，维持了市场的高波动性。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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