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https://hdl.handle.net/10419/32175
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Voev, Valeri | en |
dc.date.accessioned | 2007-04-26 | - |
dc.date.accessioned | 2010-05-14T12:00:40Z | - |
dc.date.available | 2010-05-14T12:00:40Z | - |
dc.date.issued | 2007 | - |
dc.identifier.pi | urn:nbn:de:bsz:352-opus-32379 | en |
dc.identifier.uri | http://hdl.handle.net/10419/32175 | - |
dc.description.abstract | Modelling and forecasting the covariance of financial return series has always been a challenge due to the so-called curse of dimensionality. This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold-Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models. | en |
dc.language.iso | eng | en |
dc.publisher | |aUniversity of Konstanz, Center of Finance and Econometrics (CoFE) |cKonstanz | en |
dc.relation.ispartofseries | |aCoFE Discussion Paper |x07/01 | en |
dc.subject.ddc | 330 | en |
dc.subject.stw | Varianzanalyse | en |
dc.subject.stw | Zeitreihenanalyse | en |
dc.subject.stw | Kapitalertrag | en |
dc.subject.stw | Prognoseverfahren | en |
dc.subject.stw | Theorie | en |
dc.title | Dynamic modeling of large dimensional covariance matrices | - |
dc.type | |aWorking Paper | en |
dc.identifier.ppn | 527906778 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:cofedp:0701 | - |
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