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

Research output: Working paper



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.