Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally shrunk by recombining sample eigenvectors with a (potentially nonlinear) function of the unobservable population covariance matrix. The optimal shape of this function reflects the loss/risk that is...

Financial Engineering war gestern. Mit steigendem Renditedruckrücken Finanzinvestoren näher an ihre Managementteams heran.Sie ernennen qualifizierte Beiräte, beeinflussen die Strategieund engagieren sich über Berater auch im Tagesgeschäft.

This paper reviews important concepts and methods that are useful for hypothesis testing.First, we discuss the Neyman-Pearson framework. Various approaches to optimalityare presented, including finite-sample and large-sample optimality. Then, some of the mostimportant methods are summarized, as...

Consider the problem of testing s hypotheses simultaneously. In order to deal with themultiplicity problem, the classical approach is to restrict attention to procedures that controlthe familywise error rate (FWE). Typically, it is known how to construct tests of the individualhypotheses, and...

Fund-of-funds (FoF) managers face the task of selecting a (relatively) small number ofhedge funds from a large universe of candidate funds. We analyse whether such a selectioncan be successfully achieved by looking at the track records of the available funds alone,using advanced statistical...

Consider the problem of testing s hypotheses simultaneously. In this paper, we derivemethods which control the generalized familywise error rate given by the probability ofk or more false rejections, abbreviated k-FWER. We derive both single-step and stepdownprocedures that control the k-FWER in...