How to Stress-Test an Indexed Universal Life (IUL) Policy Under Different Market Conditions

An IUL illustration gives you a projection of how a policy could perform under a particular set of assumptions. But if you want to understand how the policy might behave in the real world, one market path isn't enough.

A better approach is to run the policy through a library of different market paths — including strong markets, weak markets, crashes at different points in the policy, prolonged periods of poor performance, and periods of unusually high volatility.

If you already have an Excel model that accurately reproduces the calculations in your IUL illustration, you can do this yourself. The basic methodology is straightforward: keep the policy assumptions and mechanics unchanged, replace the illustrated index returns with different market paths, and compare what happens.

If you don't have that kind of model, you can simply upload the illustration to our analyzer and it will perform the analysis for you.

What markets should you stress-test an IUL against?

A useful stress test should cover several fundamentally different ways the market can behave.

For example:

• Strong market — Markets that grow 10% a year over the whole period, one with small swings from year to year and one with large ones.
• Weak market — Markets that grow only 2% a year, far more slowly than history has averaged, again one with small swings and one with large ones.
• Flat market — A market that does not grow at all over the period and moves little from year to year. Nothing dramatic happens; the growth simply is not there.
• Lost decade — The first 10 years end where they began, with moderate swings along the way, and the market grows 6% a year after that. A crash is over in two years; a lost decade gives the account 10 years with nothing to compound.
• Crash in the early years — A severe fall in years 4 and 5, about 40% over the two years, then recovery. It lands when the account is smallest, so the dollars lost are few — but the years of compounding lost with them are the most.
• Crash midway through — The same crash, moved to years 24 and 25. Comparing it with the early and late versions is how you see that WHEN a fall happens matters as much as how big it is.
• Crash near the end — The same crash again, in years 44 and 45. It lands on a much larger balance, with fewer years to recover.
• More than one crash — The same crash twice, in years 4 and 5 and again in years 24 and 25, with recovery between them.
• Market history data — The S&P 500's own annual price returns over the most recent years, in the order they actually happened. The same sequence is also run reversed, to study the sequence-of-returns risk.

All 4 crash markets are built on the same underlying market, which grows 6% a year with moderate swings, so the only difference between them is where the crashes fall. The lost decade is built on that same market.

These conditions are not mutually exclusive. A market path can, for example, be both strong over the long run and unusually volatile.

Here is the library we run: 12 market paths, 2 each for the strong market, the weak market and the market history data, and 1 each for the flat market, the lost decade, the 3 crash timings and the market with more than one crash. Open any path to see what it did year by year.

Why does the order of good and bad years matter?

Because two market paths with exactly the same yearly results can put a policy in very different places, depending on when the bad years fall.

One of the most useful things you can do with a market-path library is separate return risk from sequence-of-returns risk.

Suppose two market paths contain exactly the same annual returns. In one path, the worst years happen near the beginning. In the other, they happen near the end.

The long-term CAGR is identical. The volatility is identical. But the policy may behave very differently.

This is especially important for an IUL because the policy isn't simply an investment account that compounds the market return. Annual crediting, caps, floors, charges, cash-value growth, loans, and the timing of withdrawals can all make the path matter.

That means a proper stress test should include more than different long-term CAGRs. It should deliberately rearrange the same underlying returns to see what changes when the order changes.

Historical data can be particularly useful here. You can take an actual historical sequence, reverse it, reshuffle it, or create new sequences by drawing blocks of historical returns. These variations preserve much of the character of the original data while allowing you to examine how sensitive the policy is to sequence.

Does every stress-test market need to be realistic?

Not every scenario needs to be a realistic forecast.

In fact, some of the most useful scenarios are deliberately constructed to answer a specific question.

For example, take one market path and create three versions by moving the same major crash to the beginning, middle, and end. If the resulting CAGRs and volatility remain essentially the same, any difference in the IUL's outcome is primarily telling you about when the crash happened.

Likewise, you might hold volatility roughly constant while varying the long-term return, or hold long-term return roughly constant while changing the amount of volatility.

This turns the stress test into a series of controlled experiments rather than a collection of arbitrary "bull" and "bear" cases.

What you can learn from the same data

Once you have the market paths running through your IUL model, you can use them to answer much more than "how much cash value will I have?"

For example, you can examine:

• How much the cash value varies across different market conditions.
• Whether and when the policy lapses under unfavorable paths.
• How much you can safely borrow at different points in the policy's life.
• How sensitive the death benefit is to different market sequences.
• How the policy compares with investing the same premiums yourself.
• How the comparison changes when you account for the cost of separate term insurance.
• How much the outcome changes when crediting assumptions become less favorable or policy charges increase.
• Whether a policy's apparent long-term performance depends heavily on a particular sequence of market returns.

You can also summarize the results within each type of market condition. For some questions, a typical outcome is useful; for others, the most adverse path matters more. A policy that looks fine on average but lapses under one plausible sequence deserves a different level of scrutiny than one that remains sustainable across the entire test set.

What do stress test results tell you about an IUL?

How the policy behaves if a given market happens, not how likely that market is.

A scenario library should not be interpreted as a prediction of what the market will do.

If three of your scenarios involve crashes, that does not mean you have concluded that crashes have a one-in-three probability. The scenarios are selected because they expose different characteristics of the policy.

The purpose is not to predict the next 40 years.

It is to ask a more useful question:

What happens to this particular policy when the market behaves in different ways?

That distinction matters. A stress test is most valuable when it shows you where the policy is robust, where its results are sensitive to timing or assumptions, and where a seemingly attractive illustration depends on conditions that may not persist.

What does it take to stress-test an IUL properly?

There is one catch: doing this properly requires an IUL model that reproduces the mechanics of the actual illustration.

You need to be able to take the policy's premiums, charges, cost of insurance, crediting rules, caps, floors, loans, and other assumptions and recalculate the policy year by year. Simply multiplying the illustrated cash value by a few hypothetical market returns won't do it.

Once you have that model, however, the methodology is straightforward: feed the same policy into many different market paths and examine how the results change.

That's exactly the kind of analysis an IUL illustration alone cannot show you.

If you don't want to build the spreadsheet yourself, you can upload your IUL illustration to our analyzer below and start with a free diagnostic.