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Parametric Models and Their Tails

  • Henrik Hult
  • Filip Lindskog
  • Ola Hammarlid
  • Carl Johan Rehn
Chapter
Part of the Springer Series in Operations Research and Financial Engineering book series (ORFE)

Abstract

In this chapter we consider approaches to selecting a parametric family of distributions for a random variable and approaches to estimating the parameters. We also present techniques for analyzing the tails of the chosen probability distribution and the effect of the tails on the estimation of risk measures. Finally, we consider a semiparametric approach to the estimation of tail probabilities. It provides an alternative to relying on a full parametric model in order to produce estimates of tail probabilities beyond the range of the sample data.

Keywords

Parametric Family Tail Probability Generalize Pareto Distribution Reference Distribution Parametric Bootstrap 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    Meucci, A.: Risk and Asset Allocation. Springer, New York (2005)Google Scholar
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    Resnick, S.I.: Heavy-Tail Phenomena: Probabilistic and Statistical Modeling. Springer, New York (2007)Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Henrik Hult
    • 1
  • Filip Lindskog
    • 1
  • Ola Hammarlid
    • 2
  • Carl Johan Rehn
    • 3
  1. 1.Department of MathematicsRoyal Institute of TechnologyStockholmSweden
  2. 2.Swedbank AB (publ)StockholmSweden
  3. 3.E. Öhman J:or Fondkommission ABStockholmSweden

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