#203 | Interpreting the IUL Benchmark Index
Since I started publishing the IUL Benchmark Index back in January of last year, a lot of folks have asked me how they should interpret the results. This is a very reasonable question that I should have thought to answer a long time ago but, in some ways, I wasn’t sure how to answer it initially. After tracking the values for over a year, I think I have a pretty good sense of what it means and how folks should see what it does and, equally as importantly, what it doesn’t do.
The initial goal of the IUL Benchmark Index (let’s just call it the “Benchmark” for simplicity) was to provide some sense of the current market pricing for Indexed UL caps, but that’s nearly an impossible goal to achieve. As I’ve written about in previous articles, setting caps is a dark art that every company performs differently. I can confidently say that there is no company in the industry, not a single one, that prices their caps purely to market. How do I know? Because if they did, they’d change their rates every month and no company does that. Instead, every company has some sort of cap smoothing mechanism that allow it to maintain caps for a certain period of time under some set of assumptions. There is literally no way for me to model a smoothing mechanism for caps these days because there are just too many variables at play, not the least of which is inflated policy charges being used to prop up caps at numerous insurers. And, to make matters even more complicated, each insurer has its own portfolio yield and doesn’t price its option budget to the market either.
As a result, I knew from the get-go that it was functionally impossible to create a benchmark for Indexed UL caps as they are today because I couldn’t capture all of the dark arts that go into setting caps at each insurer. Even though carriers like to present the surface of the product as glassy-smooth, there’s a lot of turbid water moving beneath the surface. Whether carriers show it or not, market forces are constantly at play and impacting the price of options. How long it takes for those market forces to show their faces is a matter of the smoothing mechanism employed by the carrier.
Accurately modeling the underlying market forces is actually pretty easy. I built a hypothetical investment portfolio based on book value yields, which is how a life insurer looks at fixed income assets, based on the Moody’s Aaa and Moody’s Baa indices. I chose a 70/30 blend to reflect the general credit quality of a typical life insurance portfolio. These indices don’t capture any exotic investment strategies that an insurer might employ because, of course, those strategies are non-standard. But more importantly, those strategies also cut both ways. They might work well now but they could blow up in the future. It’s more accurate to model an option budget based on corporate bond yields because they always form the bedrock of an insurer’s portfolio. Any significant deviance from these yields is up to the insurer to explain.
Determining fair-market option prices is also pretty easy. I have an Excel spreadsheet with a Black-Scholes option pricing model and I pull market option prices off of my Fidelity account (if you can’t see option prices, apply for option trading privileges on your brokerage account). The model interpolates between the listed prices on the exchange by solving for implied volatility. For example, if the S&P 500 is at 3345.78 and quotes call options are at strike prices of 3325 and 3350, then the model solves for the implied volatility at 3325 and 3350 and interpolates for the actual 0% floor call option with a strike at 3345.78. The dates rarely match up either. For example, I’m still using 1/15/2020 call options (less than 1 year to expiry) and I use the model to interpolate the 2 weeks to today’s date. As time progresses, I’ll switch to the next maturity date.
The net result is that the IUL Benchmark Index is an accurate reflection of what a life insurer could afford for a cap on an Indexed UL product using market new-money rates and market option prices. It is not a reflection of what an on-the-shelf Indexed UL product can offer on caps. So why is it useful?
I generally try to avoid using clichés but this one is just too easy – the Benchmark shows you where the puck is going, not where it is. Over the past year, the Benchmark cap has dropped from a high of 9.2% to a low of 5.8%, roughly where it is today. Caps have been decreasing as well, but not to the same degree as in the Benchmark. This is to be expected because of the smoothing mechanisms employed by the life insurers. Directionally, however, the Benchmark and the real-world were aligned. The Benchmark shows the quickly deteriorating core economics of the Indexed UL strategy. Fixed income yields dropped like a stone and corporate credit spreads compressed throughout the year, which starts to put pressure on portfolio yields. But more importantly, option prices were as unforgiving as a bootcamp sergeant, consistently staying high due to a flat yield curve and suffocating volatility skew.
However, it’s hard to pull apart the two pieces of the puzzle in the Benchmark. Falling rates mean lower caps, all else being equal, and sometimes option prices work to mitigate falling rates or exacerbate them. To separate the two components and provide a better measure of option prices over time, I started tracking the market price of a 10% cap starting back in June. That, too, has been pretty interesting to watch even though the range is less dramatic than the core Benchmark. The price of a 10% cap has been as low as 4.4% (6/20/2019) and as high as 5% (10/9/2019) but has consistently hovered around 4.8%. How the price of the 10% cap moves is less obvious than the movements in the Benchmark, but I’ve picked up on some subtle trends. LIBOR plays a backseat to volatility impacts. For example, the cheapest price for the 10% cap was in June, when LIBOR was nearly 2.5%. Right now, the price of a 10% cap is pretty similar to what it was in June but LIBOR is 1.8%.
By far, the bigger impact on the price of a 10% cap is volatility but, more importantly, volatility skew. I track volatility skew in my model and watching it change has been pretty interesting. When volatility pops, the price of the 10% cap goes up, but it’s not a linear relationship. Volatility skew ruins what might otherwise be a nice, neat story about volatility and the price of the cap. Skew effectively works against the buyer of the options. For example, when the price of the cap was 5% back in October, at-the-money volatility was 17.9% but 10% OTM volatility was 13.8%, meaning volatility skew was 23% (1-13.8% / 17.9%). On January 16th, the cost of the cap was just 4.5% but volatility skew increased to 27%. All else being equal, this increased the cost of the cap. This same phenomenon has been occurring all year. Whenever volatility increases, skew backs off just a shade but when volatility drops, then skew tightens the screws. The net result is that the price of the 10% cap ends up being a lot less related to volatility than you might think and a lot more related to this mysterious thing called volatility skew. In short, both the Benchmark and the market price of a 10% cap allow are data points for where caps will go. The way I interpret the Benchmark is that it is a leading indicator of caps over the long run. If the Benchmark stays lower than IUL caps in the market, then eventually you should see those caps start to fall. The same logic applies to the price of the 10% cap. If that cost increases or stays higher than what carriers are reasonably earning on their general account portfolios, then you can expect caps to drop. Right now, both of those measures are indicating that caps are going to continue to fall throughout 2020 – and that’s where I’d put my money, too.