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Research output: Working paper
Research output: Working paper
}
TY - UNPB
T1 - The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility
AU - Hizmeri, Rodrigo
AU - Izzeldin, Marwan
AU - Murphy, Anthony
AU - Tsionas, Mike
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
KW - Realized volatility
KW - Signed Jumps
KW - Finite Jumps
KW - Infinite Jumps
KW - Volatility Forecasts
KW - Noise-Robust Volatility
KW - Model Averaging
M3 - Working paper
T3 - Economics Working Papers Series
BT - The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility
PB - Lancaster University, Department of Economics
CY - Lancaster
ER -