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Discussion Paper Series 2: Banking and Financial Studies, Deutsche Bundesbank >

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dc.contributor.authorSchmidt, Rafaelen_US
dc.contributor.authorSchmieder, Christianen_US
dc.date.accessioned2009-01-28T16:05:04Z-
dc.date.available2009-01-28T16:05:04Z-
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/10419/19766-
dc.description.abstractThe situation of a limited availability of historical data is frequently encountered in portfolio risk estimation, especially in credit risk estimation. This makes it, for example, difficult to find temporal structures with statistical significance in the data on the single asset level. By contrast, there is often a broader availability of cross-sectional data, i.e., a large number of assets in the portfolio. This paper proposes a stochastic dynamic model which takes this situation into account. The modelling framework is based on multivariate elliptical processes which model portfolio risk via sub-portfolio specific volatility indices called portfolio risk drivers. The dynamics of the risk drivers are modelled by multiplicative error models (MEM) - as introduced by Engle (2002) - or by traditional ARMA models. The model is calibrated to Moody's KMV Credit Monitor asset returns (also known as firm-value returns) given on a monthly basis for 756 listed European companies at 115 time points from 1996 to 2005. This database is used by financial institutions to assess the credit quality of firms. The proposed risk drivers capture the volatility structure of asset returns in different industry sectors. A characteristic temporal structure of the risk drivers, cyclical as well as a seasonal, is found across all industry sectors. In addition, each risk driver exhibits idiosyncratic developments. We also identify correlations between the risk drivers and selected macroeconomic variables. These findings may improve the estimation of risk measures such as the (portfolio) Value at Risk. The proposed methods are general and can be applied to any series of multivariate asset or equity returns in finance and insurance.en_US
dc.language.isoengen_US
dc.relation.ispartofseriesDiscussion Paper, Series 2: Banking and Financial Supervision 2007,07en_US
dc.subject.jelC13en_US
dc.subject.jelC16en_US
dc.subject.jelC51en_US
dc.subject.ddc330en_US
dc.subject.keywordPortfolio risk modellingen_US
dc.subject.keywordElliptical processesen_US
dc.subject.keywordCredit risken_US
dc.subject.keywordmultiplicative error modelen_US
dc.subject.keywordvolatility clusteringen_US
dc.subject.stwPortfolio-Managementen_US
dc.subject.stwRisikoen_US
dc.subject.stwVolatilitäten_US
dc.subject.stwStochastischer Prozessen_US
dc.subject.stwKreditrisikoen_US
dc.subject.stwSchätzungen_US
dc.subject.stwTheorieen_US
dc.subject.stwWelten_US
dc.titleModelling dynamic portfolio risk using risk drivers of elliptical processesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn533619017en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:bubdp2:5608-
Appears in Collections:Discussion Paper Series 2: Banking and Financial Studies, Deutsche Bundesbank

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