The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility
48 Pages Posted: 22 Apr 2019 Last revised: 29 Apr 2020
Date Written: 2019-03-28
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 significantly 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.
Keywords: Realized Volatility, Signed Jumps, Finite Jumps, Infinite Jumps, Volatility Forecasts, Noise-Robust Volatility, Model Averaging
JEL Classification: C22, C51, C53, C58
Suggested Citation: Suggested Citation