Asset correlations play an important role in credit portfolio modelling. One possible data source for their estimation are default time series. This study investigates the systematic error that is made if the exposure pool underlying a default time series is assumed to be homogeneous when in reality it is not. We find that the asset correlation will always be underestimated if homogeneity with respect to the probability of default (PD) is wrongly assumed, and the error is the larger the more spread out the PD is within the exposure pool. If the exposure pool is inhomogeneous with respect to the asset correlation itself then the error may be going in both directions, but for most PD-and asset correlation ranges relevant in practice the asset correlation is systematically underestimated. Both effects stack up and the error tends to become even larger if in addition we assume a negative correlation between asset correlation and PD within the exposure pool, an assumption that is plausible in many circumstances and consistent with the Basel RWA formula. It is argued that the generic inhomogeneity effect described in this paper is one of the reasons why asset correlations measured from default data tend to be lower than asset correlations derived from asset value data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.