The Effects of Asset Return Correlation Errors in the Creditmetrics Framework

Abstract: To determine risk of a bond portfolio one might assume that the obligors' asset returns follow a multivariate normal distribution with certain correlations. We investigate how errors in the estimates of these correlations aect portfolio risk measures, where the CreditMetrics framework is used to model portfolio behavior. Monte Carlo simulations are carried out on two sample portfolios, with long and short durations respectively. The correlation structures are altered both systematically and randomly and we also perform simulations assuming independence and near perfect correlations. When the correlations are changed at random we nd that the risk measures remain almost unaltered, which indicates robustness of the CreditMetrics framework. Increased asset return correlations across all obligors lead to higher volatilities and Value-at-Risks. The eect on the volatilities are small, whereas the Value-at-Risks dier to a greater extent. This re　ects the need for greater capital buers, which is not recognized by the volatility measure. One can thus conclude that the volatility is not an optimal risk measure for credit portfolios and that accurate correlation estimates are important in order to determine suitable capital buers.