Modeling returns volatility: Realized GARCH incorporating realized risk measure
Jianfeng Li and
Physica A: Statistical Mechanics and its Applications, 2018, vol. 500, issue C, 249-258
This study applies realized GARCH models by introducing several risk measures of intraday returns into the measurement equation, to model the daily volatility of E-mini S&P 500 index futures returns. Besides using the conventional realized measures, realized volatility and realized kernel as our benchmarks, we also use generalized realized risk measures, realized absolute deviation, and two realized tail risk measures, realized value-at-risk and realized expected shortfall. The empirical results show that realized GARCH models using the generalized realized risk measures provide better volatility estimation for the in-sample and substantial improvement in volatility forecasting for the out-of-sample. In particular, the realized expected shortfall performs best for all of the alternative realized measures. Our empirical results reveal that future volatility may be more attributable to present losses (risk measures). The results are robust to different sample estimation windows.
Keywords: High-frequency data; Volatility; Realized GARCH; Realized risk measures (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:500:y:2018:i:c:p:249-258
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