The empirical evidence of heavy tails in stock return data is recognised by risk managers as an important factor in assessing the Value-at-Risk and risk profile of investment portfolios. Tail index estimation appears to be a tailor-made tool for estimating the extreme quantiles of heavy tailed...

In this work we propose a new estimator for Zenga's inequality measure in heavy tailed populations. The new estimator is based on the Weissman estimator for high quantiles. We will show that, under fairly general conditions, it has asymptotic normal distribution. Further we present the results...

Accurate modeling of extreme price changes is vital to financial risk management. We examine the small sample properties of adaptive tail index estimators under the class of student-t marginal distribution functions including GARCH and propose a model-based bias-corrected estimation approach....

The aim of this paper is to give a formal definition and consistent estimates of the extremes of a population. This definition relies on a threshold value that delimits the extremes and on the uniform convergence of the distribution of these extremes to a Pareto type distribution. The tail...

AMS classifications: 62G20, 62G32;

In this paper we study the finite sample behavior of the Hill estimator under α-stable distributions. Using large Monte Carlo simulations we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce...

Despite its wide use, the Hill estimator and its plot remain to be difficult to use in Extreme Value Theory (EVT) due to substantial sampling variations in extreme sample quantiles. In this paper, we propose a new plot we call the eigenvalue plot which can be seen as a generalization of the Hill...

Power-law tail behavior and the summation scheme of Levy-stable (alpha- stable) distributions is the basis for their frequent use as models when fat tails above a Gaussian distribution are observed. However, recent studies suggest that financial asset returns exhibit tail exponents well above...

Power-law tail behavior and the summation scheme of Levy-stable distributions is the basis for their frequent use as models when fat tails above a Gaussian distribution are observed. However, recent studies suggest that financial asset returns exhibit tail exponents well above the Levy-stable...

In this paper we study the finite sample behavior of the Hill estimator under α-stable distributions. Using large Monte Carlo simulations we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce...