 Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial dataJin-Yu Fu, Jin-Guan Lin and Hong-Xia Hao International Journal of Forecasting, 2023, vol. 39, issue 4, 1698-1712 Abstract: This paper introduces a model that can accommodate both the continuous-time-diffusion and discrete-time mixed-GARCH–Jump models by embedding the discrete mixed-GARCH-Jump structure in the continuous volatility process. The key feature of the proposed model is that the corresponding conditional integrated volatility adopts the mixed-GARCH-Jump structure that accounts for the effect of jumps on future volatility. A Griddy–Gibbs sampler approach is proposed to estimate parameters, and volatility forecasting and value-at-risk forecasting based on the peaks-over-threshold method are developed. Simulations are carried out to check the finite sample performance of the proposed methodology, and empirical studies show that, in general, volatility is heavily influenced by the continuous innovations, rather than the extreme reactions. We find that both the simulation and empirical results in most cases support the proposed model. Keywords: Itô process; GARCH model; Additive jumps; Griddy–Gibbs sampler; Volatility and VaR forecasting; Peaks over threshold (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:4:p:1698-1712 DOI: 10.1016/j.ijforecast.2022.08.006 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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