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Solvency assessment within the ORSA framework: issues and quantitative methodologies


  • Julien Vedani

    () (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Laurent Devineau

    () (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)


The implementation of the Own Risk and Solvency Assessment is a critical issue raised by Pillar II of Solvency II framework. In particular the Overall Solvency Needs calculation left the Insurance companies to define an optimal entity-specific solvency constraint on a multi-year time horizon. In a life insurance society framework, the intuitive approaches to answer this problem can sometimes lead to new implementation issues linked to the highly stochastic nature of the methodologies used to project a company Net Asset Value over several years. One alternative approach can be the use of polynomial proxies to replicate the outcomes of this variable throughout the time horizon. Polynomial functions are already considered as efficient replication methodologies for the Net Asset Value over 1 year. The Curve Fitting and Least Squares Monte-Carlo procedures are the best-known examples of such procedures. In this article we introduce a possibility of adaptation for these methodologies to be used on a multi-year time horizon, in order to assess the Overall Solvency Needs.

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  • Julien Vedani & Laurent Devineau, 2012. "Solvency assessment within the ORSA framework: issues and quantitative methodologies," Working Papers hal-00744351, HAL.
  • Handle: RePEc:hal:wpaper:hal-00744351
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    References listed on IDEAS

    1. Rulliere, Didier & Loisel, Stephane, 2004. "Another look at the Picard-Lefevre formula for finite-time ruin probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 187-203, October.
    2. Laurent Devineau & Stéphane Loisel, 2009. "Risk aggregation in Solvency II: How to converge the approaches of the internal models and those of the standard formula?," Post-Print hal-00403662, HAL.
    3. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    4. Nteukam T., Oberlain & Planchet, Frédéric, 2012. "Stochastic evaluation of life insurance contracts: Model point on asset trajectories and measurement of the error related to aggregation," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 624-631.
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    Cited by:

    1. Piotr Komański & Oskar Sokoliński, 2015. "Least-Squares Monte Carlo Simulation for Time Value of Options and Guarantees Calculation," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 41.


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