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...

Markowitz portfolio selection is a cornerstone in finance, both in academia and in the industry. Most academic studies either ignore transaction costs or account for them in a way that is both unrealistic and suboptimal by (i) assuming transaction costs to be constant across stocks and (ii)...

Return event studies generally involve several companies but there are also cases when only one company is involved. This makes the relevant testing problems, abnormal return (AR) and cumulative abnormal return (CAR), more difficult since one cannot exploit the multitude of companies (by using a...