Over the years I’ve written numerous articles about the challenges posed by fixed income attribution, be they unreliable data, a lack of widely accepted attribution models, or a market evolving faster than performance measurement teams could keep up. There was always a reliable well of first-hand experience to go back to for that “Aha!” moment.
As global central banks unwind pandemic era support and (gasp) move away from low or negative policy rates, it is safe to say that fixed income attribution has entered a new age. Today, it has less to do with the models in use, many of which have evolved beyond the needs of fund managers, and more to do with telling a comprehensive story in a difficult yield environment.
The fixed income attribution tale today needs to balance classic backward-looking performance attribution, new asset types functioning as substitute core positions, and a forward-looking component to try to look around the corner.
Let’s start where we finished, back when rates typically only fell, and a core fixed income strategy could extend a little on duration and maybe take a bit more credit risk relative to an aggregate-like benchmark to earn alpha.
Dear reader, the models in use then still work today! But layering on yield curve positioning is more imperative when short rates have jumped 250 basis points (bps) in a year while long rates are “only” up 90 bps. So too, decomposing spread management becomes a more meaningful decision. Are we using a spread duration and option-adjusted spread (OAS) approach? Or a duration-times-spread (DTS) approach that takes account of the proportionality of spread levels and term structure? If we are using a model-derived price for private debt, is our liquidity premium tethered to the current environment?
The answer is likely to be as driven by portfolio construction as it is client preference. Structured securities, bank loans, real return assets, and hybrid securities like preferreds all behave differently in a rising rate environment. “Yields go up, prices go down” remains a basic mantra of fixed income investing, but the devil is in the details as complexity reigns.
The first screenshot below illustrates a common, yet outdated view. We can see one instance of this in the price return calculation—it’s not detailed enough. Is this driven by overall interest rate levels, yield curve positioning, or spread management? Is effective duration being used instead of key rate durations, or are more key rates needed to sufficiently analyze the yield curve positioning? The method chosen may be material to fully understand the performance during the measurement period.
The second screenshot tells a more comprehensive story. Here we decompose price return into accretion, rolldown, shift, twist, shape, volatility, and spread changes. We enhance the story by including key rate durations and spread levels along with a visual representation of DTS or duration times spread (OAS on the x-axis, spread duration on the y-axis).
Financial innovation has always drawn me to fixed income. These innovations include the emergence of private debt, the intricacies of modeling a collateralized loan obligation (CLO), and even understanding the value of treasury inflation-protected securities (TIPS) in an inflationary environment. There is never a dull moment.
That innovation comes with a practical cost. Pricing is often imperfect and model derived. Cash flow waterfalls evolve rapidly. Direct versus indirect exposure becomes a sticking point around what is the “correct” way to measure it.
The infrastructure surrounding the attribution model becomes the secret sauce. Need to automate matrix pricing for your direct lending or private placement portfolio? What about adjusting collateral-level recovery assumptions in response to the headlines? And that direct vs. indirect exposure problem? Whether the portfolio structure is a complex multi-strategy vehicle or simply expanding a real estate investment trust (REIT) to the underlying issuers, a simple radio button makes the difficult second nature.
It is also possible to warn portfolio managers of looming risks. What do sector returns look like if the glide path to a neutral rate is concentrated over the rest of 2022? How does U.S. dollar strength impact foreign exchange (FX) pairs? How do you model collateral taking a massive haircut anyway? In turn, each of those is a challenging question where the answer undoubtedly starts with “it depends.” That said, at the time of writing in mid-May, all three of those are questions being asked today. There are now tools to gain real insight into a fixed income portfolio, not just tell the story.
For example, in the previous low-rate environment in the Eurozone, carry return was minimal. However, FX volatility has been a major contributor to the risk/return profile as the U.S. dollar has ripped higher. Depending on a portfolio’s base currency and the portfolio manager’s decision to hedge the currency exposure, currency could be either a major impact for performance or a non-event.
As the market continues to evolve, models can now incorporate a broad range of fixed income products, from vanilla derivatives (such as interest rate swaps and Treasury futures) to FX overlay strategies to more complex securities (such as the credit default swap index [CDX] or the plethora of asset-backed securities of all shapes and sizes).
It is now possible to carry out effective scenario analysis to understand the impact of including different instruments on the composition of yield and total return over a bespoke time horizon and volatility regime. What is a CDX or currency swap likely to do to the fund? From this lens, attribution is simply one tool in the kit. Layering on ex ante capability becomes a true differentiator for managing ever more complex fixed income portfolios.
A shift in inflation expectations and the unwinding of quantitative easing will make for a different market to come. Many funds are only just building sufficient expertise to exploit the full flexibility available to them.
The fixed income market will continue to evolve. The rise in real and nominal rates, the emergence of new asset types and lenders, and the demand of investors to know more about their portfolios faster than ever before all require an update to the toolkit, even if the starting spot feels familiar.
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