 Modeling and forecasting realized volatility: getting the most out of the jump componentAdam Clements and Yin Liao () No 93, NCER Working Paper Series from National Centre for Econometric Research Abstract: Modeling and forecasting realized volatility is of paramount importance. Recent econometric developments allow total volatility to be decomposed into its' constituent continuous and jump components. While previous studies have examined the role of both components in forecasting, little analysis has been undertaken into how best to harness the jump component. This paper considers how to get the most out of the jump component for the purposes of forecasting total volatility. In combination with the magnitude of past jumps, the intensity of jump occurrence is examined. Estimated jump intensity from a point process model is used within a forecasting regression framework. Even in the presence of the diffusive part of total volatility, and past jump size, intensity is found to significantly improve forecast accuracy. The improvement is particularly apparent on the days when jumps occur or when markets are turbulent. Overall, the best way to harness the jump component for volatility forecasting is to make use of both the magnitude and probability of jump occurrences. Keywords: Realized volatility; diffusion; jumps; point process; Hawkes process; forecasting (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:qut:auncer:2013_5 Access Statistics for this paper More papers in NCER Working Paper Series from National Centre for Econometric Research Contact information at EDIRC. |
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