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- handle: 10670/1.qge9v1
- isbn: 978-3-030-54252-8
- hal: hal-03186680
- doi: 10.1007/978-3-030-54252-8_14
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-54252-8_14
Mots-clés
EPD expected shortfall financial risks GPD hill pareto quantile rare events regular variation reinsurance second order value-at-risk C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C13 - Estimation: General C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C18 - Methodological Issues: General C - Mathematical and Quantitative Methods/C.C4 - Econometric and Statistical Methods: Special Topics/C.C4.C46 - Specific Distributions • Specific Statistics G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G22 - Insurance • Insurance Companies • Actuarial Studies G - Financial Economics/G.G3 - Corporate Finance and Governance/G.G3.G32 - Financing Policy • Financial Risk and Risk Management • Capital and Ownership Structure • Value of Firms • GoodwillCiter ce document
Arthur Charpentier et al., « Pareto Models for Risk Management », HAL-SHS : économie et finance, ID : 10.1007/978-3-030-54252-8_14
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Résumé
The Pareto model is very popular in risk management, since simple analytical formulas can be derived for financial downside risk measures (value-at-risk, expected shortfall) or reinsurance premiums and related quantities (large claim index, return period). Nevertheless, in practice, distributions are (strictly) Pareto only in the tails, above (possible very) large threshold. Therefore, it could be interesting to take into account second-order behavior to provide a better fit. In this article, we present how to go from a strict Pareto model to Pareto-type distributions. We discuss inference, derive formulas for various measures and indices, and finally provide applications on insurance losses and financial risks.