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 policies) with minimum guarantees. 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.

Financial evaluation of life insurance policies in high performance computing environments

Stefania Corsaro;Pasquale Luigi De Angelis;Zelda Marino;Paolo Zanetti
2012

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 policies) with minimum guarantees. 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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/30476
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact