Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptivemethod to choose the number of order statistics involved in an optimal way, balancing variance and biascomponents. Recently this has been achieved for the similar but somewhat less involved...

We use a subsample bootstrap method to get a consistent estimate of the asymptotically optimal choice of the samplefraction, in the sense of minimal mean squared error, which is needed for tail index estimation. Unlike previous methodsour procedure is fully self contained. In particular, the...

We show that two prominent bootstrap tests for fund skill have distorted test sizes because many funds have short return records and skewed return residuals, and they lack test power to detect skilled funds when a substantial number of unskilled funds are present. We develop the theory for a...