Moment estimators for autocorrelated time series and their application to default correlations

In credit risk modeling, method-of-moment approaches are popular for estimating;latent asset return correlations within and between rating buckets. However, the autocorrelation often present in time series of default rates leads to estimations that are systematically too low. We propose a new estimator that adjusts to the problems of;this autocorrelation and the shortness of the time series, thus eliminating a significant portion of the bias observed with classical estimators. The adjustment is based on convergence and approximation results for general autocorrelated time series, and it is easily implementable and nonparametric.