Spatial dependence in stock returns - Local normalization and VaR forecasts

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.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.identifier.uri	http://hdl.handle.net/2003/30307
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5391
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

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