Chances are, your financial portfolio does not include life insurance products.  Robert Danielsen, CEO of Cynametrix, a financial analytics consulting firm based in Minnesota, thinks this should change.

He explains that life insurance, or Insured Death Benefit (IDB) is a useful class for optimizing portfolios because its rate of return is driven primarily by when death occurs, rather than fluctuations in the capital markets, which means that IDB returns have a low correlation to those of other asset classes that are dependent on the stock market’s performance.

According to modern portfolio theory, adding an asset which has returns that are not tightly correlated to other assets in that portfolio can mitigate the risk of overall loss for that portfolio by reducing volatility–thus, adding life insurance into the mix can help act as a ‘shock absorber’ for market-driven stocks and other financial products.

While IDBs are beneficial, incorporating them is tricky. They can manifest in two separate ways—either as cash value— the investment element of a policy that is paid to the owner if the policy is surrendered prior to death, or as the death benefit, which is the amount paid to the policy’s beneficiary at the insured’s death. “The problem is, when you evaluate an IDB it’s hard to compare it to securities,” says  Danielsen. “What are you going to evaluate? The cash build-up or death benefit? Historically, insurance people have talked about the two characteristics separately.”

Optimizing portfolios with life insurance also presents a problem. Danielsen explains that, for portfolios with traditional financial products, analysts use the annual expected mean return and variance of an asset class to optimize the return or minimize portfolio instability. However, with life insurance products “there’s only one point of return—that’s the death of the insured, and we don’t know when that is—so how do you integrate that?”

Danielson has devised a novel method of evaluating and optimizing IDB known as MAV^sm (which stands for mortality adjusted value) Using @RISK‘s Monte Carlo simulations, he process generates values and indexes for valuing the cash position of any life insurance product or strategy, incorporating mortality probability.

Along with this IDB data, the final result of Danielsen’s MAV^sm model also includes a distribution of thousands of possibilities for the securities portfolio for each year of the strategy. Using this projection of probable values allows Danielsen to see the overall risk-adjusted returns of the portfolio.

Thanks to Palisade software, Danielsen has been able to make planning as precise as possible. “Palisade products have really enhanced these optimizing methods for me, and helped me create a much more sophisticated product.”

Read the full case study here.