Abstract
When estimating high-dimensional PD correlation matrices from short times series the estimation error hinders the detection of a signal. We smooth the empirical correlation matrix by reducing the dimension of the parameter space from quadratic to linear order with respect to the dimension of the underlying random vector. Using the method by Plerou et al. (2002) we present evidence for a one-factor model. Using the noise-reduced correlation matrix leads to increased security of the economic capital estimate as estimated using the credit risk portfolio model CreditRisk+.
The work of Rafael Weißbach has been supported by the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 475.
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Weißbach, R., Rosenow, B. (2005). Smooth Correlation Estimation with Application to Portfolio Credit Risk. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_55
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DOI: https://doi.org/10.1007/3-540-28084-7_55
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