Robust analysis of default intensity

The problem of robust estimation and multivariate outlier detection of the term structure of default intensity is considered. Both themultivariate Vasicek andCIRmodels, embedding the Kalman filter algorithm in a forward search context, are used to estimate default intensity. The focus is not on the estimation of credit models including jumps, but on the automatic detection ofmaskedmultiple outliers inmultivariate time series. Both simulated and real market credit spread time series are analyzed. In order to make inference on outliers, confidence envelopes which are virtually independent of the estimated parameters are introduced. The output is not only a unique default intensity term structure curve, as often used in the financial literature, but a robust confidence intervalwithinwhich default intensity is likely to stay. © 2011 Elsevier B.V. All rights reserved.