The term structure of American interest rates is filtered to reduce the influence of cross correlations and auto correlations on its factors. A three-factor model is fitted to the filtered data. Contrary to most studies of the term structure on monthly data, performing statistical tests we...

We consider time series models in which the conditional mean of the response variable given thepast depends on latent covariates. We assume that the covariates can be estimated consistentlyand use an iterative nonparametric kernel smoothing procedure for estimating the conditional meanfunction....

State price density (SPD) contains important information concerning market expectations. In existing literature, a constrained estimator of the SPD is found by nonlinear least squares in a suitable Sobolev space...

We propose a simple class of semiparametric multivariate GARCH models, allowing for asymmetric volatilities and time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of estimates for averaged correlations...

From a banking supervisory perspective, this paper analyses aspects of market risk of anaggregated trading portfolio comprised of the trading books of 11 German banks with aregulatory approved internal market risk model. Based on real, clean prot and loss dataand Value-at-Risk estimates of the...

Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numericalrepresentation is much less transparent. In classical rating models a convenientrepresentation of ratings in a closed form is possible reducing...

In the era of Basel II a powerful tool for bankruptcy prognosis isvital for banks. The tool must be precise but also easily adaptable tothe bank's objections regarding the relation of false acceptances (TypeI error) and false rejections (Type II error). We explore the suitabil-ity of Smooth...

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive Classification Tree (BACT), whichextends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric...

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of...

We consider the problem of estimating the conditional quantile of a time series at time t given observations of the same and perhaps other time series availableat time t − 1. We discuss sieve estimates which are a nonparametric versions ofthe Koenker-Bassett regression quantiles and do not...