- LancasterWP2019_008
855 KB, PDF document

Research output: Working paper

Published

**The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility.** / Hizmeri, Rodrigo; Izzeldin, Marwan; Murphy, Anthony; Tsionas, Mike.

Research output: Working paper

Hizmeri, R, Izzeldin, M, Murphy, A & Tsionas, M 2019 'The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility' Economics Working Papers Series, Lancaster University, Department of Economics, Lancaster.

Hizmeri, R., Izzeldin, M., Murphy, A., & Tsionas, M. (2019). *The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility*. (Economics Working Papers Series). Lancaster University, Department of Economics.

Hizmeri R, Izzeldin M, Murphy A, Tsionas M. The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility. Lancaster: Lancaster University, Department of Economics. 2019 May 1. (Economics Working Papers Series).

@techreport{fc5950f79ab64893ad89995c90ba897d,

title = "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility",

abstract = "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.",

keywords = "Realized volatility, Signed Jumps, Finite Jumps, Infi nite Jumps, Volatility Forecasts, Noise-Robust Volatility, Model Averaging",

author = "Rodrigo Hizmeri and Marwan Izzeldin and Anthony Murphy and Mike Tsionas",

year = "2019",

month = may,

day = "1",

language = "English",

series = "Economics Working Papers Series",

publisher = "Lancaster University, Department of Economics",

type = "WorkingPaper",

institution = "Lancaster University, Department of Economics",

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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 -