We study the drift of stationary diffusion processes in a time series analysis of the autoregression function. A marked empirical process measures the difference between the nonparametric regression functions of two time series. We bootstrap the distribution of a Kolmogorov-Smirnov-type test...

In banking the default behavior of the counterpart is of interest not only for the pricing of transactions under credit risk but also for the assessment of portfolio credit risk. We develop a test against the hypothesis that default intensities are constant over time within a homogeneous group...

Multivariate equivalence testing becomes necessary whenever the similarity rather than a difference between several treatment groups with multiple endpoints has to be shown. This problem occurs in various applications, including bioequivalence or the comparison of dissolution profiles....

Most credit portfolio models calculate the loss distribution of a portfolio consisting solely of performing counterparts. We develop two models that account for defaulted counterparts in the calculation of the economic capital. First, we model the portfolio of non-performing counterparts...

Proofs for the consistency of the kernel density estimator have historically developed. Four important milestones are the pointwise consistency, the almost sure uniform convergence, the rate of convergence on a bounded interval and the rate of convergence on R. The underlying concepts of total...

The risk of a credit portfolio depends crucially on correlations between latent covariates, for instance the probability of default (PD) in different economic sectors. Often, correlations have to be estimated from relatively short time series, and the resulting estimation error hinders the...

This paper introduces a test for zero correlation in situations where the correlation matrix is large compared to the sample size. The test statistic is the sum of the squared correlation coefficients in the sample. We derive its limiting null distribution as the number of variables as well as...