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Semiparametric Tail Index Regression [PDF]
Journal of Business & Economic Statistics, 2020 Abstract–Understanding why extreme events occur is often of major scientific interest in many fields. The occurrence of these events naturally depends on explanatory variables, but there is a severe lack of flexible models with asymptotic theory for understanding this dependence, especially when variables can affect the outcome nonlinearly.
Rui Li, Chenlei Leng, Jinhong You
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Regression Estimator for the Tail Index [PDF]
Journal of Statistical Theory and Practice, 2020 AbstractEstimating the tail index parameter is one of the primal objectives in extreme value theory. For heavy-tailed distributions the Hill estimator is the most popular way to estimate this parameter. Several recent publications’ aim was to improve the Hill estimator, using different methods, for example the bootstrap, or the Kolmogorov–Smirnov ...
László Németh, András Zempléni
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Tail index estimation in the presence of covariates: Stock returns’ tail risk dynamics
Journal of Econometrics, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nicolau, João +2 more
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Tail-Index Estimates in Small Samples [PDF]
Journal of Business and Economic Statistics, 2001 Financial returns are known to be nonnormal and tend to have fat-tailed distributions. This article presents a simple methodology that accurately estimates the degree of tail fatness, characterized by the tail index, in small samples. Our method is a weighted average of Hill estimators for different threshold values that corrects for the small-sample ...
Ronald Huisman, Kees G Koedijk
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Simultaneous tail index estimation
Revstat Statistical Journal, 2004 The estimation of the extreme-value index γ based on a sample of independent and identically distributed random variables has received considerable attention in the extreme-value literature.
Jan Beirlant , Yuri Goegebeur
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Adjusted empirical likelihood method for the tail index of a heavy-tailed distribution [PDF]
Statistics & Probability Letters, 2019 Empirical likelihood is a well-known nonparametric method in statistics and has been widely applied in statistical inference. The method has been employed by Lu and Peng (2002) to constructing confidence intervals for the tail index of a heavy-tailed distribution.
Yizeng Li, Yongcheng Qi
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A New Regression-Based Tail Index Estimator
The Review of Economics and Statistics, 2019 Abstract A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail ...
Nicolau, João, Rodrigues, Paulo M. M.
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A Diagnostic Plot for Estimating the Tail Index of a Distribution [PDF]
Journal of Computational and Graphical Statistics, 2004 The problem of estimating the tail index in heavy-tailed distributions is very important in many applications. We propose a new graphical method that deals with this problem by selecting an appropriate number of upper order statistics. We also investigate the method's theoretical properties are investigated.
Bruno DE SOUSA
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Dissecting the Multivariate Extremal Index and Tail Dependence
Revstat Statistical Journal, 2020 A central issue in the theory of extreme values focuses on suitable conditions such that the well[1]known results for the limiting distributions of the maximum of i.i.d. sequences can be applied to stationary ones.
Helena Ferreira , Marta Ferreira
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On tail index estimation based on multivariate data [PDF]
Journal of Nonparametric Statistics, 2015 This article is devoted to the study of tail index estimation based on i.i.d. multivariate observations, drawn from a standard heavy-tailed distribution, i.e. of which 1-d Pareto-like marginals share the same tail index. A multivariate Central Limit Theorem for a random vector, whose components correspond to (possibly dependent) Hill estimators of the ...
Clémençon, Stéphan, Dematteo, Antoine
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