The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility

We document the forecasting gains achieved by incorporating measures of signed, finite, and infinite jumps in forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that vary by sector, volume and degree of jump activity. We use extended HAR-RV models, and consider different frequencies (5, 60, and 300 seconds), forecast horizons (1, 5, 22,and 66 days) and the use of standard and robust-to-noise volatility and threshold bipower variation measures. Incorporating signed finite and infinite jumps generates signfiicantly better real-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility measures at the 300 second frequency generate the smallest real-time mean squared forecast errors. Finally, the forecasts from simple model averages generally outperform forecasts from the single best model