Many different robust estimation approaches for the covariance or shape matrix of multivariate data have been established until today. Tyler's M-estimator has been recognized as the "most robust" M-estimator for the shape matrix of elliptically symmetric distributed data. Tyler's Mestimators for...

We develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm....

In recent publications standard methods of random matrix theory have been applied to principal components analysis of high-dimensional financial data. We discuss the fundamental results and potential shortcomings of random matrix theory in the light of the stylized facts of empirical finance. It...

We develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm....

It has been frequently observed in the literature that many multivariate statistical methods require the covariance or dispersion matrix S of an elliptical distribution only up to some scaling constant. If the topic of interest is not the scale but only the shape of the elliptical distribution,...

Suppose that we are searching for the maximum of many unknown and analytically untractable quantities or, say, the "best alternative" among several candidates. If our decision is based on historical or simulated data there is some sort of selection bias and it is not evident if our choice is...