Market efficiency and random number generators in Solvency II

Francesco Strati ()
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Francesco Strati: KPMG Advisory SpA, Actuarial Services, Milan, Italy

International Journal of Financial Engineering (IJFE), 2022, vol. 09, issue 02, 1-17

Abstract: Complex insurance policies are valued by Economic Scenario Generators (ESG), stochastic simulations developed via Monte Carlo techniques that span the evolution of risk factors important for insurers (financial and technical) consistent with market data. The either pseudo or quasi-random number generators used in the model turn out to be important for the precision of the valuation of Asset/Liability models. I shall compare five different random number generators in a G2++ model in terms of market efficiency, by testing for the fulfilment of the martingale condition. The enhancement of existing methods is crucial in the field of computational insurance based on Monte Carlo techniques given that the number of simulations is perilously limited.

Keywords: Monte Carlo; Quasi-Monte Carlo; pseudo-random numbers; actuarial studies (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S2424786321500286

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