Filtering a nonlinear stochastic volatility model

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dc.contributor.authorElliott, R.
dc.contributor.authorSiu, T.
dc.contributor.authorFung, E.
dc.date.issued2012
dc.description.abstractWe introduce a class of stochastic volatility models whose parameters are modulated by a hidden nonlinear dynamical system. Our aim is to incorporate the impact of economic cycles, or business cycles, into the long-term behavior of volatility dynamics. We develop a discrete-time nonlinear filter for the estimation of the hidden volatility and the nonlinear dynamical system based on return observations. By exploiting the technique of a reference probability measure we derive filters for the hidden volatility and the nonlinear dynamical system.
dc.description.statementofresponsibilityRobert J. Elliott, Tak Kuen Siu and Eric S. Fung
dc.identifier.citationNonlinear Dynamics, 2012; 67(2):1295-1313
dc.identifier.doi10.1007/s11071-011-0069-4
dc.identifier.issn0924-090X
dc.identifier.issn1573-269X
dc.identifier.urihttp://hdl.handle.net/2440/69983
dc.language.isoen
dc.publisherKluwer Academic Publ
dc.relation.grantARC
dc.rights© Springer Science+Business Media B.V. 2011
dc.source.urihttps://doi.org/10.1007/s11071-011-0069-4
dc.subjectStochastic volatility
dc.subjectNonlinear dynamical system
dc.subjectEconomic cycles
dc.subjectNonlinear filters
dc.subjectChange of measures
dc.subjectReference probability
dc.titleFiltering a nonlinear stochastic volatility model
dc.typeJournal article
pubs.publication-statusPublished

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