Introduction

The VIX (CBOE Volatility Index) measures implied volatility of S&P 500 options, capturing market uncertainty. But VIX can lag realizations of fear during flash crashes. Twitter sentiment—proportion of fearful tweets mentioning market, stocks, or crisis—offers a real-time alternative fear gauge. Comparing Twitter fear-gauge to VIX reveals which is more predictive of actual market volatility and returns.

Twitter Sentiment Extraction

Collect tweets mentioning "market," "crash," "crash," "recession" during trading hours. Use fine-tuned BERT models to classify sentiment as fearful, neutral, or bullish. Aggregate into daily Twitter Fear-Gauge (0-100 scale). Compare to VIX levels and subsequent realized volatility. Results: Twitter fear-gauge spikes often slightly lead VIX spikes, suggesting Twitter captures market sentiment faster.

Predictive Comparison

Regress realized volatility (actual stock price swings) on lagged VIX and lagged Twitter Fear-Gauge. Using Granger causality tests, determine which has more predictive power. Incorporate both into ensemble models to forecast 1-week-ahead volatility. Ensemble outperforms using either alone.

Conclusion

Twitter fear-gauge complements VIX by capturing real-time sentiment shifts, jointly improving volatility forecasts.