Financial econometrics and big data: A survey of volatility estimators and tests for the presence of jumps and co-jumps

Mukherjee, Arpita; Peng, Weijia; Swanson, Norman R.; Yang, Xiye

doi:10.1016/bs.host.2018.11.006

Cited by 10 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar qualitative conclusions are obtained using the multijump test ofCaporin et al (2017). The number of detected cojumps is also similar to the numbers reported inCaporin et al (2017) andMukherjee et al (2020).…”

supporting

confidence: 86%

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

Bu¹,

Hizmeri²,

Izzeldin³

et al. 2020

View full text Add to dashboard Cite

supporting

confidence: 86%

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

Bu¹,

Hizmeri²,

Izzeldin³

et al. 2020

View full text Add to dashboard Cite

“…Across a range of different asset classes, they find that 5-minute returns volatilities obtained from the two-scale realized volatility (TSRV) subsampling approach ofZhang et al (2005) is the preferred method of estimating daily volatility.4 Similar qualitative conclusion are obtained using the multijump test ofCaporin et al (2017). The number of detected cojumps is also similar to the numbers reported inCaporin et al (2017) andMukherjee et al (2020).…”

mentioning

confidence: 52%

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

Bu¹,

Hizmeri²,

Izzeldin³

et al. 2022

View full text Add to dashboard Cite

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-second 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.

show abstract

“…For literature in this area, see (Barndorff-Nielsen and Shephard 2006;Lee and Mykland 2007;Jiang and Oomen 2008;Aït-Sahalia et al 2009;Huang and Tauchen 2005;Mancini 2009;Podolskij and Ziggel 2010;Corradi et al 2018;Boswijk et al 2018;Mukherjee et al 2020, and the references cited therein).…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Co-Jumps, Co-Jump Tests, and Volatility Forecasting: Monte Carlo and Empirical Evidence

Peng

Yao²

2022

JRFM

Self Cite

View full text Add to dashboard Cite

This study classifies jumps into idiosyncratic jumps and co-jumps to quantitatively identify systematic risk and idiosyncratic risk by utilizing high-frequency data. We found that systematic risk occurs more frequently and has larger magnitudes than the idiosyncratic risk in an individual asset, which indicates that volatilities from one sector are largely derived from the contagious effect of other sectors. We further investigated the importance of idiosyncratic jumps and co-jumps to predict the sector-level S&P500 exchange-traded fund (ETF) volatility. It was found that the predictive content of co-jumps is higher than that of idiosyncratic jumps, suggesting that systematic risk is more informative than idiosyncratic risk in volatility forecasting. Additionally, we carried out Monte Carlo experiments designed to examine the relative performances of the four co-jump tests. The findings indicate that the BLT test and the co-exceedance rule of the LM test outperform other tests, while the co-exceedance rule of the LM test has larger power and a smaller empirical size than that of the BLT test.

show abstract

Financial econometrics and big data: A survey of volatility estimators and tests for the presence of jumps and co-jumps

Cited by 10 publications

References 50 publications

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

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

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

Co-Jumps, Co-Jump Tests, and Volatility Forecasting: Monte Carlo and Empirical Evidence

Contact Info

Product

Resources

About