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Parameter Estimation in Stable Law


  • Annika Krutto

    () (Institute of Mathematics and Statistics, University of Tartu, J. Liivi Str 2, Tartu 50409, Estonia)


For general stable distribution, cumulant function based parameter estimators are proposed. Extensive simulation experiments are carried out to validate the effectiveness of the estimates over the entire parameter space. An application to non-life insurance losses distribution is made.

Suggested Citation

  • Annika Krutto, 2016. "Parameter Estimation in Stable Law," Risks, MDPI, Open Access Journal, vol. 4(4), pages 1-15, November.
  • Handle: RePEc:gam:jrisks:v:4:y:2016:i:4:p:43-:d:83700

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    References listed on IDEAS

    1. Szymon Borak & Wolfgang Härdle & Rafal Weron, 2005. "Stable Distributions," SFB 649 Discussion Papers SFB649DP2005-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Knight, John L. & Satchell, Stephen E., 1997. "The Cumulant Generating Function Estimation Method," Econometric Theory, Cambridge University Press, vol. 13(02), pages 170-184, April.
    3. Jun Yu, 2004. "Empirical Characteristic Function Estimation and Its Applications," Econometric Reviews, Taylor & Francis Journals, vol. 23(2), pages 93-123.
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    More about this item


    bootstrap; characteristic function; cumulant function; parameter estimation; simulation; severity distribution;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting
    • K2 - Law and Economics - - Regulation and Business Law


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