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

Research output: Working paper

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

Lancaster : Lancaster University, Department of Economics, 2019. (Economics Working Papers Series).

Research output: Working paper

Harvard

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.

APA

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.

Vancouver

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).

Author

Hizmeri, Rodrigo ; Izzeldin, Marwan ; Murphy, Anthony ; Tsionas, Mike. / The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility. Lancaster : Lancaster University, Department of Economics, 2019. (Economics Working Papers Series).

Bibtex

@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",

}

RIS

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 -