Using Monte Carlo to Assess Variable Life

Katt, Peter
July 2005
Journal of Financial Planning;Jul2005, Vol. 18 Issue 7, p26
Academic Journal
This article relates the experience of the author in using Monte Carlo simulations to assess variable life (VL) insurance. Unfortunately, almost all sellers of VL have not the vaguest notion of how inaccurate their illustrated premiums are. This is passed on to buyers who bond with these premiums that have no chance of being right. Buyers usually do not reassess this situation until disaster is about to strike. To dislodge clients from their loyalty to the illustrated premium, in 2004 our firm began routinely running Monte Carlo simulations to determine the chances a VL policy will fail if the illustrated premium is followed. To test VL, we extract the mortality and expense components of the tested policy, and its premiums, and apply an appropriate arithmetic average investment return with a standard deviation for investment volatility. Our Monte Carlo results are fascinating. For a client who had bought a level-death-benefit variable life with an illustrated target premium several months before hiring us, we found the probabilities of policy failure were 20 percent, 35 percent and 48 percent based on average equity fixed-account yields of 12 percent, 10 percent and 8 percent respectively. Certainly Monte Carlo testing is based on mathematical principles to determine the chances certain events will occur. It will be a significant miscarriage of justice if 12 percent constant yields are left standing as appropriate, while the far more sophisticated and accurate Monte Carlo testing is determined to have been out of bounds. It is hard to imagine a better tool for assessing the risk and reward balance of a specific financial transaction and to define investor suitability.


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