A computational proposal for a robust estimation of the Pareto tail index: An application to emerging markets

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Abstract

In this work, we backtest and compare, under the VaR risk measure, the fitting performances of three classes of density distributions (Gaussian, Stable and Pareto) with respect to three different types of emerging markets: Egypt, Qatar and Mexico. We also propose a new technique for the estimation of the Pareto tail index by means of the Threshold Accepting (TAVaR) and the Hybrid Particle Swarm Optimization algorithm (H-PSOVaR). Furthermore, we test the accuracy and robustness of our estimates demonstrating the effectiveness of the proposed approach.
Lingua originaleEnglish
pagine (da-a)108048-
Numero di pagine16
RivistaApplied Soft Computing
Volume114
Stato di pubblicazionePublished - 2022

All Science Journal Classification (ASJC) codes

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