Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists
General Finance
2019-06-04 v1 Pricing of Securities
Abstract
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
Cite
@article{arxiv.1906.00059,
title = {Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists},
author = {Jozef Barunik and Cathy Yi-Hsuan Chen and Jan Vecer},
journal= {arXiv preprint arXiv:1906.00059},
year = {2019}
}