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dc.contributor.authorDing, Y.
dc.date.accessioned2022-01-04T17:05:04Z
dc.date.available2022-01-04T17:05:04Z
dc.date.issued2021-11-08
dc.identifier.otherCWPE2179
dc.identifier.otherJIWP2111
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331922
dc.description.abstractWe propose a new class of conditional heteroskedasticity in the volatility (CHV) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then introduce two examples of CH-V in which we specify a GJR-GARCH and an E-GARCH processes for the volatility of volatility, respectively. We also show a novel way of introducing the leverage effect of negative returns on the volatility through the volatility of volatility process. Empirical study confirms that CH-V models have better goodness-of-fit and out-of-sample volatility and Value-at-Risk forecasts than common GARCH-type models.
dc.publisherFaculty of Economics, University of Cambridge
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectGARCH
dc.subjectSHARV
dc.subjectvolatility
dc.subjectvolatility of volatility
dc.subjectforecasting
dc.titleConditional Heteroskedasticity in the Volatility of Asset Returns
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.79371


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