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Financial Decision Making Using Computational Intelligence pp 281–319Cite as

Financial Evaluation of Life Insurance Policies in High Performance Computing Environments

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 70))

Abstract

The European Directive Solvency II has increased the request of stochastic asset–liability management models for insurance undertakings. The Directive has established that insurance undertakings can develop their own “internal models” for the evaluation of values and risks in the contracts. In this chapter, we give an overview on some computational issues related to internal models. The analysis is carried out on “Italian style” profit-sharing life insurance policies (PS policy) with minimum guaranteed return. We describe some approaches for the development of accurate and efficient algorithms for their simulation. In particular, we discuss the development of parallel software procedures. Main computational kernels arising in models employed in this framework are stochastic differential equations (SDEs) and high-dimensional integrals. We show how one can develop accurate and efficient procedures for PS policies simulation applying different numerical methods for SDEs and techniques for accelerating Monte Carlo simulations for the evaluation of the integrals. Moreover, we show that the choice of an appropriate probability measure provides a significative gain in terms of accuracy.

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Notes

  1. 1.

    In the following formulas, for sake of brevity, it is understood that all the expected values are conditional expectations.

  2. 2.

    We do not deal with a ratings-based model, in the form of [21, 22, 31]. We do not consider the potential of upgrades or downgrades of the underlying bonds, which would result in a shift of the default intensity to that of the new rating. We use constant credit quality corporate bond indices to estimate the dynamics of the default intensities.

  3. 3.

    We refer to criteria applied by ECB when selecting bonds for the estimation of yield curves (www.ecb.int/stats/money/yc/html/index.en.html).

  4. 4.

    Source Bloomberg.

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Authors and Affiliations

  1. Dipartimento di Statistica e Matematica per la Ricerca Economica, Università degli Studi di Napoli “Parthenope”, via Medina 40, 80133, Napoli, Italy

    Stefania Corsaro, Pasquale Luigi De Angelis, Zelda Marino & Paolo Zanetti

Authors
  1. Stefania Corsaro
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  2. Pasquale Luigi De Angelis
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  3. Zelda Marino
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  4. Paolo Zanetti
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Correspondence to Stefania Corsaro .

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Editors and Affiliations

  1. Dept. Production Engineering, and Management, Technical University of Crete, University Campus, Chania, 731 00, Greece

    Michael Doumpos

  2. , Department of Production Engineering and, Technical University of Crete, University Campus, Chania, 73100, Greece

    Constantin Zopounidis

  3. , Department of Industrial and Systems Eng, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

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Corsaro, S., De Angelis, P.L., Marino, Z., Zanetti, P. (2012). Financial Evaluation of Life Insurance Policies in High Performance Computing Environments. In: Doumpos, M., Zopounidis, C., Pardalos, P. (eds) Financial Decision Making Using Computational Intelligence. Springer Optimization and Its Applications, vol 70. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3773-4_11

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