Suchergebnisse

Determining contributions by sub-portfolios or single exposures to portfolio-wide economic capital for credit risk is an important risk measurement task. Often economic capital is measured as Value-at-Risk (VaR) of the portfolio loss distribution. For many of the credit portfolio risk models used in practice, the VaR contributions then have to be estimated from Monte Carlo samples. In the context of a partly continuous loss distribution (i.e. continuous except for a positive point mass on zero), we investigate how to combine kernel estimation methods with importance sampling
to achieve more efficient (i.e. less volatile) estimation of VaR contributions.

We discuss the coherence properties of expected shortfall (ES) as a financial risk measure. This statistic arises in a natural way from the estimation of the 'average of the 100% worst losses' in a sample of returns to a portfolio. Here p is some fixed confidence level. We also compare several alternative representations of ES which turn out to be more appropriate for certain purposes(J.E.L.: G20, C13, C14).