What impact have rising interest rates and the cessation of US Libor had on rates desks?
Both developments have had drastic impact on trading desks and their supporting middle office risk teams, although in very different ways. For a start, the displacement of USD Libor has been a long and drawn-out process, with market participants preparing for this eventuality over the last three to four years. Many market participants are ready for the shift, but it is a global event impacting desks from the US, to Europe, Latin America to APAC. Some markets, such as the Singapore dollar markets or Thai bhat, see teams still uncertain of how to navigate this process even though we are only a few months away from the official end of the legacy USD rate. Even in regions such as the US, while it appears preparations have gone well, the act of shifting to a new rate has longer term implications from a curve analytics perspective.
In contrast, the rise in interest rates to tackle inflation has happened very quickly in the established developed financial centres, with interest rates increasing from close to zero percent or negative to over three percent in the EU and the UK, whereas it is now flirting with five percent in the US. On some level, the impact has been positive. Monetary policy changes and the impact of adjustments in key debt and rate markets, such as US Treasuries and SOFR futures, has created a more volatile market, leading to surging volumes and greater opportunity for generating trading revenues.
But there are risks, too. What has happened recently with Silicon Valley Bank is a perfect example of how rapidly rising interest affects bond prices and interest rate curves. In this example, the valuation of the bank’s fixed duration bond portfolio decreased significantly creating the conditions for the poor financial health of their balance sheet. Improper interest rate hedge proves are no longer seamless. In other institutions, trading desks have seen the rate curves become unstable.
What are the potential hurdles institutions must overcome in this current climate?
We have just exited a 10-year period of very low or negative rates. The curve was extremely flat, and rates are now climbing. We see much steeper rate curve shapes, particularly on the short end. This can be stressful in terms of how firms interpret rates or predict rates in between quoted points.
During this long period of low rates, the need to continuously invest in curve analytics was not particularly tangible. Keeping some basic curve construction techniques which worked during these past years was tempting. However, starting last year, it began to fail—it led to many diverse challenges around bogus forwards and subsequent pricing and risk challenges.
The bigger institutions did care about model validation processes that made them ready for such curve steepening scenarios. Smaller institutions and non-banks might have neglected this part and should now rapidly shift into new interpolation methods. This is where the main hurdle lies.
Murex has invested considerable effort unpacking simplistic curve assumptions and strengthening construction techniques to manage steep curves.
In what ways can adjustments to interest rate curves increase instability for trading?
These sudden series of shocks on policy rates drastically redefined the usual interest rate curves properties of the upward sloping rate curve. As a result, we now see an inverted curve with expectations that this could get steeper in the short term as the market anticipates more changes to the base interest rate in key markets, before reverting to a more typical sloping curve in the medium term. This is evidenced by looking at SOFR, EURIBOR and €STR forward curves.
The challenge for trading desks and middle office teams is that the steepened curve creates additional constraints to the resulting curve. When multiplied across numerous curves, future rate predictions are challenged, making trading unstable. Current interpolation methods have failed to provide smooth rate curves, and insufficient interpolation approaches will generate instabilities when part of the curve is highly steep and constrained.
The impact can be drastic. Some firms say their swaps prices are just wrong and not matching their sophisticated counterparts. These firms were using very basic curve construction techniques, acceptable enough to handle previous’ years situation but now becoming hazardous to accurately value their positions. In addition to this valuation perspective, simple interpolation schemes can also dangerously affect risk figures with respect to the market quote inputs, since the deformation of the curve resulting from scenarios performed on these quotes is distorted. They must observe what has worked elsewhere and implement solutions quickly to the most pressing problems.
What approach should trading desks and risk teams take to better manage the volatile rate environment?
While technology can bring the necessary tools to better adjust curves and stabilise trading, the strategy for implementing the technology is equally important. It is essential that financial institutions centralise their curve analytics internally.
A financial institution uses rate curves in most business processes. But these entities often have several curve analytics systems. Fixing everything at once and preserving consistency is a challenge.
Firms need to look at the systems and analytics architecture and choose single source of truth, to borrow a phrase from the DeFi universe. Firms need to figure out how to ensure consistency across the organisation so that whenever there is a change, they only have to change it once, creating minimal impact for the trading desk. When it comes to curves, disruption is the norm and change is the only constant.
Another aspect is model validation. The maturity of this process depends on the firm. We are helping firms think strategically about model validation. Firms should never assume things will be static. A buffer to manage the unexpected is critical. Changes always come, even in a less extreme interest rate dynamic.
How can technology help financial institutions navigate these market challenges?
Centralising curve analytics entails putting a REST API on top of the Murex rate curve module. This exposes curve analytics in a deeply simple API that any organisational system can use. Firms can minimise reconciliation costs and adapt quickly to upcoming market changes. This single source of truth can be plugged into any other system and avoids the redundancy of multiple similar programs doing the same calculation.
When it comes to model validation, whether they use an in-house or vendor system, firms must invest in the right people to understand the curve and put best practices in place. APIs are required on the tech side to retrieve historical data, and to be able to manipulate data to build belief that analytics models will be solid.
Technology must be part of a firm’s ongoing investment. It is part of being current and uncompromising on the tools used. Workarounds can solve tactical problems in the short term. But these workarounds accumulate and might eventually demand a painfully intensive upgrade. Continually and gradually upgrading tools is a better approach. In extreme market environments, such as the one we are seeing today, the workaround approach and technology set-up will be found lacking.