Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists
29 Pages Posted: 13 Jun 2019
Date Written: May 31, 2019
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.
Keywords: High frequency text, Sentiment, Stochastic volatility, Continuous time models
JEL Classification: G12, C14, C51, C58, G4
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