This paper examines latent risk factors in models for migration risk. We employ thestandard statistical framework for ordered categorical variables and induce dependencebetween migrations by means of latent risk factors. By assuming a Markov process forthe dynamics of the latent factors, the...

We propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilitiesand correlations. The model estimation is feasible in large dimensions and the positive definiteness of the conditional...

We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest...

The American put is one of the oldest problems in mathematical finance. We review the development of the relevant literature over the last 40 years. Today the mainstream computational problems have been solved satisfactorily and the target of research is shifting towards the development of...

We propose a class of new robust GMM tests for endogenous structural breaks. The tests are based on supremum and average statistics derived from robust GMM estimators with a bounded influence function. They imply a bounded linearized asymptotic bias of size and power under local model...

We propose a new class of test statistics inducing accurate dual likelihood ratio tests of parametric constraints in overidentified moment conditions models. These statistics are derived from the dual likelihood implied by the exponent in the saddlepoint approximation of a general GMM estimator...