首页|A computational proposal for a robust estimation of the Pareto tail index: An application to emerging markets
A computational proposal for a robust estimation of the Pareto tail index: An application to emerging markets
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NSTL
Elsevier
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. (C) 2021 Elsevier B.V. All rights reserved.
MetaheuristicsPareto-type distributionTail index estimationValue-at-RiskEmerging marketsDISTRIBUTIONSBEHAVIORREGIMESLAW
NSTL
Elsevier
