Abstract

The valuation of large variable annuity portfolios is an important enterprise risk management task but is computationally challenging due to the need for simulation. Existing methods in the literature only use simple experimental designs with significant room for improvement. This article identifies three major components in an efficient valuation framework. In addition, we propose optimal experimental designs and provides analytical insights for each component. Our numerical results show that our proposal achieves significantly higher accuracy than state-of-the-art alternatives without requiring any additional computational resource.

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