Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Guhr, Thomas | - |
dc.contributor.author | Schäfer, Rudi | - |
dc.contributor.author | Schmitt, Thilo A. | - |
dc.contributor.author | Wied, Dominik | - |
dc.date.accessioned | 2013-05-07T15:15:11Z | - |
dc.date.available | 2013-05-07T15:15:11Z | - |
dc.date.issued | 2013-05-07 | - |
dc.identifier.uri | http://hdl.handle.net/2003/30307 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-5391 | - |
dc.description.abstract | We analyze a recently proposed spatial autoregressive model for stock returns and compare it to a one-factor model and the sample covariance matrix. The influence of refinements to these covariance estimation methods is studied. We employ power mapping as a noise reduction technique for the correlations. Further, we address the empirically observed non-stationary behavior of stock returns. Local normalization strips the time series of changing trends and fluctuating volatilities. As an alternative method, we consider a GARCH fit. In the context of portfolio optimization, we find that the spatial model has the best match between the estimated and realized risk measures. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | Discussion Paper / SFB 823;18/2013 | en |
dc.subject.ddc | 310 | - |
dc.subject.ddc | 330 | - |
dc.subject.ddc | 620 | - |
dc.title | Spatial dependence in stock returns - Local normalization and VaR forecasts | en |
dc.type | Text | de |
dc.type.publicationtype | workingPaper | de |
dcterms.accessRights | open access | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_1813_SFB823_Schmitt_Schäfer_Wied_Guhr.pdf | DNB | 300.19 kB | Adobe PDF | View/Open |
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