Please use this identifier to cite or link to this item: https://doi.org/10.1198/jasa.2010.ap09039
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dc.titleLocalized realized volatility modeling
dc.contributor.authorChen, Y.
dc.contributor.authorHärdle, W.K.
dc.contributor.authorPigorsch, U.
dc.date.accessioned2014-10-28T05:13:00Z
dc.date.available2014-10-28T05:13:00Z
dc.date.issued2010-12
dc.identifier.citationChen, Y., Härdle, W.K., Pigorsch, U. (2010-12). Localized realized volatility modeling. Journal of the American Statistical Association 105 (492) : 1376-1393. ScholarBank@NUS Repository. https://doi.org/10.1198/jasa.2010.ap09039
dc.identifier.issn01621459
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105208
dc.description.abstractWith the recent availability of high-frequency financial data the long-range dependence of volatility regained researchers' interest and has led to the consideration of long-memory models for volatility. The long-range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short-memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern. In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. A simulation study shows that long memory processes as well as short memory processes with structural breaks can be well approximated by this local approach. Furthermore, using S&P500 data we find that our local modeling approach outperforms long-memory type models and models with structural breaks in terms of predictability. © 2010.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1198/jasa.2010.ap09039
dc.sourceScopus
dc.subjectAdaptive procedure
dc.subjectLocalized autoregressive modeling
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1198/jasa.2010.ap09039
dc.description.sourcetitleJournal of the American Statistical Association
dc.description.volume105
dc.description.issue492
dc.description.page1376-1393
dc.identifier.isiut000286545900008
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