# Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists

## TL;DR

This paper introduces a novel high-frequency sentiment-driven stochastic volatility model that quantifies market sentiment from news and analyzes its impact on volatility dynamics, providing closed-form formulas and empirical insights.

## Contribution

It develops a unified continuous-time model linking news sentiment, price, and volatility, with calibration methods and empirical validation using high-frequency news data.

## Key findings

- News sentiment increases volatility reversion threshold.
- Sentiment sustains high market volatility.
- Closed-form formulas for volatility moments.

## 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.