Analytical calculation of risk measures for variable annuity guaranteed benefits

With the increasing complexity of investment options in life insurance, more and more life insurers have adopted stochastic modeling methods for the assessment and management of insurance and financial risks. The most prevalent approach in market practice, Monte Carlo simulation, has been observed to be time consuming and sometimes extremely costly. In this paper we propose alternative analytical methods for the calculation of risk measures for variable annuity guaranteed benefits on a stand-alone basis. The techniques for analytical calculations are based on the study of geometric Brownian motion and its integral. Another novelty of the paper is to propose a quantitative model which assesses both market risk on the liability side and revenue risk on the asset side in the same framework from the viewpoint of risk management. As we demonstrate by numerous examples on quantile risk measure and conditional tail expectation, the methods and numerical algorithms developed in this paper appear to be both accurate and computationally efficient.

► It is widely acknowledged that Monte Carlo simulations impose heavy cost on insurers. ► We propose alternative methods for the risk management of guaranteed benefits. ► A quantitative model is presented to analyze both liability and revenue risks. ► Numerical examples are provided for risk measures such as VaR and CTE. ► New computational algorithms are shown to be very accurate and efficient.