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Do jumps help in forecasting the density of returns?

dc.contributor.authorChevallier, Julien
HAL ID: 7536
dc.contributor.authorIelpo, Florian
dc.contributor.authorSévi, Benoît
HAL ID: 3591
dc.date.accessioned2011-07-26T14:46:08Z
dc.date.available2011-07-26T14:46:08Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6805
dc.language.isoenen
dc.subjectbivariate modelen
dc.subjectmedian realized volatilityen
dc.subjectbipower variationen
dc.subjectrealized volatilityen
dc.subjectjumpsen
dc.subjectdensity forecastingen
dc.subject.ddc332en
dc.subject.classificationjelG1en
dc.subject.classificationjelC53en
dc.subject.classificationjelC32en
dc.subject.classificationjelC15en
dc.titleDo jumps help in forecasting the density of returns?en
dc.typeCommunication / Conférence
dc.description.abstractenThe estimation of the jump component in asset pricing has witnessed a considerably growing body of literature. Of particular interest is the decomposition of total volatility between its continuous and jump components. Recent contributions highlight the importance of the jump component in forecasting the volatility at different horizons. In this paper, we extend the methodology developed by Maheu and McCurdy (2011) to measure the information content of intraday data in forecasting the density of returns at horizons up to sixty days. We extract jumps as in Andersen, Bollerslev, Frederiksen and Nielsen (2010) to have a measure of the jumps in returns. Then, we estimate a bivariate model of returns and volatilities where the jump component is indepen- dently modeled. Our empirical results for S&P 500 futures, WTI crude oil futures, the USD/JPY exchange rate and the MacDonald’s stock confirm the importance of considering the continuous/jump decomposition for density forecasting.en
dc.identifier.citationpages45en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelEconomie financièreen
dc.relation.conftitleEEA-ESEM
dc.relation.confdate2011-08
dc.relation.confcityOslo
dc.relation.confcountryNorvège


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