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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility. / Bu, Ruijun; Hizmeri, Rodrigo; Izzeldin, Marwan et al.
In: Journal of Empirical Finance, Vol. 70, 31.01.2023, p. 144-164.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Bu, R, Hizmeri, R, Izzeldin, M, Murphy, A & Tsionas, M 2023, 'The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility', Journal of Empirical Finance, vol. 70, pp. 144-164. https://doi.org/10.1016/j.jempfin.2022.12.001

APA

Bu, R., Hizmeri, R., Izzeldin, M., Murphy, A., & Tsionas, M. (2023). The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility. Journal of Empirical Finance, 70, 144-164. https://doi.org/10.1016/j.jempfin.2022.12.001

Vancouver

Bu R, Hizmeri R, Izzeldin M, Murphy A, Tsionas M. The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility. Journal of Empirical Finance. 2023 Jan 31;70:144-164. Epub 2022 Dec 15. doi: 10.1016/j.jempfin.2022.12.001

Author

Bu, Ruijun ; Hizmeri, Rodrigo ; Izzeldin, Marwan et al. / The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility. In: Journal of Empirical Finance. 2023 ; Vol. 70. pp. 144-164.

Bibtex

@article{9598e7a560314f65a9687281ccc3786c,
title = "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility",
abstract = "We propose a novel approach to decompose realized jump measures by type of activity (finite/infinite) and sign, and also provide noise-robust versions of the ABD jump test (Andersen et al., 2007b) and realized semivariance measures. We find that infinite (finite) jumps improve the forecasts at shorter (longer) horizons; but the contribution of signed jumps is limited. As expected, noise-robust measures deliver substantial forecast improvements at higher sampling frequencies, although standard volatility measures at the 300-s frequency generate the smallest MSPEs. Since no single model dominates across sampling frequency and forecasting horizon, we show that model averaged volatility forecasts – using time-varying weights and models from the model confidence set – generally outperform forecasts from both the benchmark and single best extended HAR model. Finally, forecasts using volatility and jump measures based on transaction sampling are inferior to the forecasts from clock-based sampling.",
keywords = "Volatility forecasting, Jump measures, Business sampling, Calendar sampling, Market microstructure noise, Model averaging",
author = "Ruijun Bu and Rodrigo Hizmeri and Marwan Izzeldin and Antony Murphy and Mike Tsionas",
year = "2023",
month = jan,
day = "31",
doi = "10.1016/j.jempfin.2022.12.001",
language = "English",
volume = "70",
pages = "144--164",
journal = "Journal of Empirical Finance",
issn = "0927-5398",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility

AU - Bu, Ruijun

AU - Hizmeri, Rodrigo

AU - Izzeldin, Marwan

AU - Murphy, Antony

AU - Tsionas, Mike

PY - 2023/1/31

Y1 - 2023/1/31

N2 - We propose a novel approach to decompose realized jump measures by type of activity (finite/infinite) and sign, and also provide noise-robust versions of the ABD jump test (Andersen et al., 2007b) and realized semivariance measures. We find that infinite (finite) jumps improve the forecasts at shorter (longer) horizons; but the contribution of signed jumps is limited. As expected, noise-robust measures deliver substantial forecast improvements at higher sampling frequencies, although standard volatility measures at the 300-s frequency generate the smallest MSPEs. Since no single model dominates across sampling frequency and forecasting horizon, we show that model averaged volatility forecasts – using time-varying weights and models from the model confidence set – generally outperform forecasts from both the benchmark and single best extended HAR model. Finally, forecasts using volatility and jump measures based on transaction sampling are inferior to the forecasts from clock-based sampling.

AB - We propose a novel approach to decompose realized jump measures by type of activity (finite/infinite) and sign, and also provide noise-robust versions of the ABD jump test (Andersen et al., 2007b) and realized semivariance measures. We find that infinite (finite) jumps improve the forecasts at shorter (longer) horizons; but the contribution of signed jumps is limited. As expected, noise-robust measures deliver substantial forecast improvements at higher sampling frequencies, although standard volatility measures at the 300-s frequency generate the smallest MSPEs. Since no single model dominates across sampling frequency and forecasting horizon, we show that model averaged volatility forecasts – using time-varying weights and models from the model confidence set – generally outperform forecasts from both the benchmark and single best extended HAR model. Finally, forecasts using volatility and jump measures based on transaction sampling are inferior to the forecasts from clock-based sampling.

KW - Volatility forecasting

KW - Jump measures

KW - Business sampling

KW - Calendar sampling

KW - Market microstructure noise

KW - Model averaging

U2 - 10.1016/j.jempfin.2022.12.001

DO - 10.1016/j.jempfin.2022.12.001

M3 - Journal article

VL - 70

SP - 144

EP - 164

JO - Journal of Empirical Finance

JF - Journal of Empirical Finance

SN - 0927-5398

ER -