An Average Time to Maturity (ATM) calculation, defined as the average remaining time to maturity for each security or contract composing a debt instrument, is one of the most commonly used measures for assessing interest rate sensitivity. In this post we will look at some examples where Average Time to Maturity (ATM) calculations can have a significant impact on the accuracy of key risk measurement metrics and accurate reporting for a few specific fixed income instruments.
It is surprising to see but of all the fund managers in the financial markets, over 75% with bond portfolios may encounter a problem of inaccuracies and would see a potential benefit in reviewing their fund reporting. For standard fixed income instruments, such as zero coupon bonds, there will not be any issues in using ATM as an indicator, yet it is still important to understand how standard reporting treats these instruments. It is when you look at more bespoke “non-vanilla” bonds in your portfolio that standard reporting with ATM can lead to imprecise portfolio metrics.
As a precursor, ATM is a good and practical indicator for investors and fund managers alike in gauging the riskiness and interest rate sensitivity of their funds as well as the underlying fund’s constituents. One of the key benefits lies in the easy “quick and dirty” implementation as a means for tracking. That being said, on the downside it may not be as precise an indicator in trying to hedge interest rate risk across the interest rate term structure, a more precise indicator would be duration or even the modified duration.
When calculating the average time to maturity (ATM) for non-vanilla bonds some rough approximations can impact the estimation of your interest rate risk sensitivity. This can be problematic and a particularly challenging scenario when either hedging or taking on some interest rate risk in your portfolio.
In our first example, we take a look at perpetual bonds, which by definition do not have predetermined maturity dates. Because these bonds don’t have a set date, a zero maturity is assigned to them in the ATM calculation by most standard reporting engines. Because this ATM metric is inaccurate and does not provide meaningful data one serious consequence is that we are commonly left with a falsely dampened interest rate sensitivity.
In our second example, we will look at callable bonds. Callable bonds will offer a higher yield for bond buyers with the understanding that the issuer may call the bond, exposing the investor to reinvestment risk, in finding another suitable alternative with similar yield characteristics. This callable feature is incredibly important for both investors (receiving a higher interest rate as compensation) and issuers who will receive funding that they might not otherwise have had access to. Using the call dates is a good approximation of the likely maturity date, very rarely till the declared maturity date. The challenge in calculating an accurate ATM is that it is most often the case for callable bonds that the common reporting metrics for most funds will use the maturity date which may be 50-80 years into the future, instead of the callable date, in the calculation of the average time to maturity. One key consequence of taking this approach is that the ATM and interest rate sensitivity will be heavily skewed to the longer side, when the reality is that historically the majority of callable bonds have in fact been called. It can be seen with these two brief examples how important it is to get to the meaning behind the numbers published in investment fund performance reporting documents and regulatory filings!
These two examples help to illustrate how the Average Time to Maturity (ATM) calculation can be incorrectly skewed, either too high or too low, but in either case causes inaccurate calculations of interest rate sensitivity for fund managers and fund investors alike. It is not necessarily very difficult to report ATM correctly, as you can see with the examples above, but standard reporting tools typically do not account for those special structures. With these significant discrepancies we’ve identified it is no surprise how important it is to be in control of your own fund reporting and not be dependent on reporting engines. Whether that means you are generating your own fund reporting in-house or outsourcing, either case will ensure your company is able to account for those details. If you are interested in identifying more about this topic or about individualized calculation within fund portfolios please see this excerpt from our recent webinar, where we have some discussion on ATM. Additionally, we would be very interested to discuss in further detail.
