Preparing for the IBOR Transition: Technology and Models

This process has revealed a number of challenges for financial markets participants, with many having to rethink their operations and technology infrastructure and adopting new technologies to help with the transition.
1 Oct, 2020

The IBOR transition impacts almost every part of the financial services industry including banking, capital markets, insurance and asset management. The imminent retirement of IBOR has forced financial institutions to conduct an end-to-end inventory of IBOR exposure. This should cover the full range of processes, models and systems, including pricing, valuation, risk management and booking. This process has revealed a number of challenges for financial markets participants, with many having to rethink their operations and technology infrastructure and adopting new technologies to help with the transition.

IT Infrastructures & Models

As a result of the transition, financial institutions will need to apply many changes to their pricing models, capital models, internal models etc. It is worth highlighting the importance of the end-user computing (EUC) tool. Most financial institutions make great use of the EUC, which is fed with all of the interest rates including IBOR. It’s important that when institutions manage their transition, they make sure that their models and the EUC is updated and aligned to the new RFR.

The move away from IBOR is a very delicate transition with many complexities, but it will also be an opportunity to consider the connection with the other regulatory streams. For example, there are many streams, such as IFRS 9 or FRTB, that have overlapping features. In the case of non-modellable risk factors, there is a clear overlap between FRTB and IBOR. As such, financial institutions may benefit by not using a siloed approach with these regulatory streams and leveraging the synergy as much as possible. For instance, using data as an example, most of the market data is common across FRTB and the IBOR transition, therefore instead of adding separate databases, banks can have one unique database to manage their market data.

Several key sectors will experience a big impact from these transition risks: firstly, in product management, where firms need to create and update new products while at the same time having to refresh the current product. From a treasury perspective there is a huge impact: for example, the internal transfer pricing that all the banks use will be affected. So too will financial and tax accounting and risk management. For example, if the capital models are impacted, banks will be considering what the potential effect will be on their internal models, and evaluating if the transition to the RFR may trigger key material changes in the model, and if so, they may require approval from the regulators.

How Technology Can Help

There are many ways in which technology can help with the IBOR Transition; outlined below are just three examples.

Contract-recognition Technologies

Some banks already use this technology and many vendors provide these services. All financial institutions, but especially banks, have a lot of paperwork they can digitise with this technology. Once you have this data in a digital format, it’s much easier to perform amendments and review contracts. This technology is not without complications because having all these contracts in a digital format means that firms need the space to store them. In some cases banks may opt for cloud storage but will need to ensure this is a safe, consistent and robust solution.

Artificial Intelligence

Artificial intelligence (AI) is very useful from a risk management perspective. Algorithms can be used to extract particular information from contracts, for example the key rates, which could then be used for pricing or risk management purposes. Before investing in any new technology, it is important for firms to evaluate the scope of what they want to achieve and do an inventory of the types of contracts they have. If their contracts are fairly standard, they could develop an AI tool that can perform most of the time-consuming, manual activities such as replacing the new interest rate in contacts. However if the contracts are customised rather than standard, AI may find them difficult to analyse and firms might require human intervention.

Robotic Process Automation

Robotic process automation (RPA) could be used to inform clients that their base rates are changing. Some examples of this might include developing machine-constructed texts for mass emailing or setting up automated chat-bots to answer frequently asked questions and initiating renegotiations. This is extremely important, particularly for legacy contacts, where there is a clear contact risk. Keeping clients informed promptly and clearly is crucial and it’s important that they understand and are comfortable with any changes firms are applying. Executing this for millions of contracts would be time-consuming and require a massive effort; however, RPA technology can make the whole process much more efficient and enables financial institutions to use their resources in a more effective way.

Building SOFR Curves

To build SOFR curves, as with any curve, one has to use market quotes. CME Group started trading futures and swaps in May 2018, so there are overnight rates, futures rates and swaps rates available to build these curves. However, there is one interesting challenge in SOFR: we now deal with future rates, which are rather unusual. Previously with futures contracts, such as Dollar future contracts, rates were settled at the beginning of the contract period, whereas the SOFR futures rate will be settled at the end of the contract period and paid in arrears. There are one-month futures and three-month futures and this future rate can be calculated as an arithmetic average, as a geometric or as compounding average using a compounding formula. The biggest challenge is that in the middle of the contract period, there’s a mix of historical data and projection. Ultimately, the rate quoted is this combination and by knowing the historical part you can infer the projected rate, which will go into a projection of fitting your curve. This is doable but this is also the first time historical rates are part of the curve building. We used to have, and still do have, swaps paid in arrears FedFunds), but swaps are quote starting and so do not have a historical part. Only futures have fixed maturity and as such have both historical and projection elements.

Since October 2018, CME has been clearing different types of SOFR swaps including SOFR overnight indexed swap (OIS) versus fixed, basis swaps versus LIBOR and FedFunds, as well as numerous maturities up to five years. At this point we have to reconsider the whole multi-curve dependency. Currently, FedFund is built together with LIBOR 3 Months and sometimes with LIBOR 6 Months but they are all entangled because LIBOR 3 Months is discounted with FedFund and FedFund is quoted as a basis swap to 3 Months, and now it will all change. For SOFR swaps you will still have to use OIS/FedFund discounting, then you will have to use FedFunds basis for swaps for completion and disentanglement and apply both FedFunds and SOFR rates simultaneously, still using bootstrapping, but with a SOFR curve instead of LIBOR 3 Month.

Modelling SOFR and SONIA Rates for Fixed Income

While building SOFR curves is a challenge, using them constitutes a further difficulty for some instruments. According to the Alternative Reference Rate Committee (ARRC) recommendation, SOFR and SONIA reference floating rate notes (FRNs) should be calculated using daily rates and paid in arrears. This is nothing new as we already have swaps using daily rates and payment in arrears but the difficulty is that FRNs are not derivatives—they are fixed income instruments and, as such, do not have a settlement period. With FRNs there is no payment delay and payment should be made on the last day of the contract period. This, however, is very difficult to calculate because according to the formula, payment has to be made on the morning of the last day of the contract period, but it may be that the last rate is not yet quoted. To deal with this, the proposed solutions are lookbacks and lockouts. To illustrate lookbacks we can consider a recommendation for the SONIA FRN rate, which is five business days’ lookback (that is, one week). For example, if all the rates that are applied to the formula were on today’s date, the rate applied to the formula is not the rate that was quoted today—it is the rate that was quoted one week ago. This presents operational difficulties because firms have to keep track of these shifts and rates that settled not only within the contract period but also a week earlier.

Another way of dealing with this is through lockouts. For SOFR FRN rates there is a two business days lockout. This means that on the last two days of the contract period the rate is fixed at that which was quoted three days before the contract ends. So you will have the same rate for three days. The ARRC recommends a combination of two business days lockout and one business day lookback for SOFR, which creates another challenge for implementation. Firms will have to be flexible and incorporate any combination of lockouts and lookbacks, especially for loans, where the recommendation is to use a compounding formula—they will be paid in arrears and will have several days’ lookbacks and lockouts. Calculating different combinations means models need to be flexible and able to deal with any type of lookbacks/lockouts combination.

Challenges of Replacing IBOR in Legacy Contracts

The biggest challenge in terms of operational and computational effort involved with the transition will be replacing IBOR in legacy contracts. As soon as fallbacks are effective, adjustments should be put in place so they can be calculated in advance and this should reflect the fair adjustment or compensation for replacement of the rate. There is still no clear consensus in the market about how this should be done. Models should be able to handle replacing one curve with another and for linear instruments, for instance with swaps, that is adjusted. For each trade, it creates the whole vertical with hedges and XVA adjustments, all of which are corresponding to this trade, so this whole vertical should now be adjusted too. Hedges should be replaced from one curve to another and the whole XVA universe should be recalculated with the new type of rate. This also affects other regulatory requirements such as the liquidity coverage ratio and net stable funding ratio. Once this is recalculated, it raises the question of whether it should this be taken into the adjustment or not.

Another problem for non-linear contracts, like swaptions, is how to get the correct vol. Currently, the volatilities used for swaptions are the LIBOR swaptions vols; this market is fairly liquid so it is available for all currencies, sometimes in normal terms and sometimes in log-normal terms. Adjusting these volatilities for the new rates is a major challenge and requires long and careful consideration.

Part 1 – The Challenges and Risks of the IBOR Transition

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