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dc.contributor.authorBarigozzi, Matteo
dc.contributor.authorBrownlees, Christian
dc.contributor.authorGallo, Giampiero
dc.contributor.authorVeredas, David
dc.date.accessioned2017-12-02T14:53:16Z
dc.date.available2017-12-02T14:53:16Z
dc.date.issued2014
dc.identifier.doi10.1016/j.jeconom.2014.05.017
dc.identifier.urihttp://hdl.handle.net/20.500.12127/5220
dc.description.abstractRealized volatilities observed across several assets show a common secular trend and some idiosyncratic pattern which we accommodate by extending the class of Multiplicative Error Models (MEMs). In our model, the common trend is estimated nonparametrically, while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The methodology is illustrated using two panels of realized volatility measures between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Results show that the shape of the common volatility trend captures the overall level of risk in the market and that the idiosyncratic dynamics have a heterogeneous degree of persistence around the trend. Out-of-sample forecasting shows that the proposed methodology improves volatility prediction over several benchmark specifications.
dc.language.isoen
dc.subjectAccounting & Finance
dc.titleDisentangling systematic and idiosyncratic dynamics in panels of volatility measures
dc.identifier.journalJournal of Econometrics
dc.source.volume182
dc.source.issue2
dc.source.beginpage364
dc.source.endpage384
vlerick.knowledgedomainAccounting & Finance
vlerick.typearticleJournal article with impact factor
dc.identifier.vperid192291
dc.identifier.vperid192292
dc.identifier.vperid192293
dc.identifier.vperid181874
dc.identifier.vpubid6469
vlerick.publicationvalue.BB


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