Soft Computing

, Volume 21, Issue 5, pp 1181–1192

Relevant applications of Monte Carlo simulation in Solvency II

  • Giuseppe Casarano
  • Gilberto Castellani
  • Luca Passalacqua
  • Francesca Perla
  • Paolo Zanetti
Methodologies and Application

DOI: 10.1007/s00500-015-1847-6

Cite this article as:
Casarano, G., Castellani, G., Passalacqua, L. et al. Soft Comput (2017) 21: 1181. doi:10.1007/s00500-015-1847-6

Abstract

The definition of solvency for insurance companies, within the European Union, is currently being revised as part of Solvency II Directive. The new definition induces revolutionary changes in the logic of control and expands the responsibilities in business management. The rationale of the fundamental measures of the Directive cannot be understood without reference to probability distribution functions. Many insurers are struggling with the realisation of a so-called “internal model” to assess risks and determine the overall solvency needs, as requested by the Directive. The quantitative assessment of the solvency position of an insurer relies on Monte Carlo simulation, in particular on nested Monte Carlo simulation that produces very hard computational and technological problems to deal with. In this paper, we address methodological and computational issues of an “internal model” designing a tractable formulation of the very complex expectations resulting from the “market-consistent” valuation of fundamental measures, such as Technical Provisions, Solvency Capital Requirement and Probability Distribution Forecast, in the solvency assessment of life insurance companies. We illustrate the software and technological solutions adopted to integrate the Disar system—an asset–liability computational system for monitoring life insurance policies—in advanced computing environments, thus meeting the demand for high computing performance that makes feasible the calculation process of the solvency measures covered by the Directive.

Keywords

Life insurance policies Monte carlo simulation Nested simulation Modelling uncertainty Stochastic models  Risk assessment Asset–liability management 

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Giuseppe Casarano
    • 1
  • Gilberto Castellani
    • 2
  • Luca Passalacqua
    • 2
  • Francesca Perla
    • 3
  • Paolo Zanetti
    • 3
  1. 1.Alef Avanced Laboratory Economics and FinanceRomeItaly
  2. 2.Department of Statistical SciencesSapienza, University of RomeRomeItaly
  3. 3.Department of Management and Quantitative StudiesUniversity of Naples “Parthenope”NaplesItaly

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