RDP 2026-03: Designing an Efficient Reference Rate: Lessons from SOFIA 1. Introduction

Interest rate benchmarks are important to the functioning of modern financial markets. For example, in Australia, the bank bill swap rate (BBSW) is used to price contracts with a notional value worth $35 trillion in the 90-day futures market alone (ASX 2024). If a benchmark is distorted, financial contracts referencing it may be mispriced, undermining market efficiency. When benchmark quality is undermined, this can erode trust in the financial system, as illustrated by the global fallout from the London Interbank Offered Rate (LIBOR) scandal.

Two elements support the sound design and monitoring of benchmarks. First, benchmark quality must be measurable. Second, benchmark design should be informed by evidence on the factors that influence benchmark quality, including specific design elements and prevailing market conditions.

Broadly, benchmark quality is defined by two key characteristics: (i) low levels of noise, implying high informational efficiency; and (ii) resilience and robustness to manipulation. These are interrelated. Manipulation introduces noise by shifting rates away from their efficient market-based value, leading to movements that subsequently reverse (e.g. Comerton-Forde and Putniņš 2011a, 2011b). However, noise can also occur independently of manipulation. For instance, certain benchmark design features may fail to smooth or trim extreme rate observations, resulting in transitory price spikes.

This paper focuses on noise – defined as transitory deviations from the efficient rate that subsequently reverse. High noise impairs informational efficiency and distorts valuations of contracts indexed to the rate. We do not address the issue of manipulation directly as the beta version of the Secured Overnight Funding Index Australia (Beta SOFIA™) is published by the Australian Securities Exchange (ASX) solely for information and testing purposes. Its use as a reference, index, or benchmark in a financial instrument or contract is prohibited. Without contractual application, there is no object to be manipulated, and the incentive for manipulation is minimal during the beta phase. Where relevant, we discuss benchmark design features that are likely to affect both noise and manipulation incentives, drawing on the theoretical framework developed by Duffie and Dworczak (2021). We empirically estimate benchmark noise and examine how benchmark design and market conditions influence noise.

Our subject is Beta SOFIA, a new reference rate currently in beta form and not yet designated as a reference rate or financial benchmark. Given its developmental status, this presents a timely opportunity to evaluate the quality of the benchmark before it is launched. Beta SOFIA is based on overnight repurchase agreements (repos) collateralised by Australian dollar-denominated Australian and state government securities. It is administered by the ASX and was developed in response to demand from market participants for a secured overnight benchmark, similar to the Secured Overnight Financing Rate (SOFR) in the United States. Unlike many jurisdictions that have replaced legacy benchmarks like LIBOR with a single overnight rate – for example, SOFR in the United States and the Sterling Overnight Index Average (SONIA) in the United Kingdom – Australia has adopted a multi-rate approach: BBSW continues to operate alongside the cash rate (the unsecured overnight rate, also known by the acronym AONIA or AUD Overnight Index Average), with Beta SOFIA under development as a secured overnight rate.

Since 2023, the ASX has been publishing Beta SOFIA using volume-weighted trimmed mean methodology. The calculation excludes the bottom 25 per cent of transaction volume based on the yield distribution. This is because the repo market acts as a market for cash and collateral. The market for collateral is when participants engage in the repo market looking for a specific security as collateral, for example if they want to sell it short. Collateral transactions trade at discount to cash transactions. So, to decrease the chance of collateral transactions affecting Beta SOFIA, the administrator takes the transactions with lowest yield out of the data.

We estimate noise in Beta SOFIA using a state-space model developed in Brugler, Khomyn and Putniņš (2025), which allows us to generate a daily time series. Using transaction-level data on the repo trades underpinning Beta SOFIA, we also estimate noise at the level of individual repo trades. We then conduct time series and panel regressions to explain noise as a function of benchmark design and market structure variables, including trading volume, market concentration, related-party transaction prevalence, and market liquidity.

Our time series analysis shows that noise in Beta SOFIA tends to increase during periods of lower transaction volumes, greater market concentration, a higher proportion of related-party transactions, or reduced liquidity. These relationships are also confirmed at the transaction level, controlling for buyer and seller identity, collateral type, and day-level fixed effects, with standard errors clustered at the collateral-day level.

A key strength of this study is the use of a unique dataset comprising the actual repo transactions used to construct Beta SOFIA. This allows us to directly observe the rate distribution and simulate counterfactual benchmark designs (e.g. applying alternative trimming thresholds). For example, under the current design, the bottom 25 per cent of transaction volume (by yield) is excluded. We find that noise can be reduced by also trimming the top 5 per cent of transactions, although this comes at the cost of reduced eligible transaction volume.

Our findings yield several changes that could enhance SOFIA's design:

• Make the reporting of related-party transactions mandatory and exclude them from the rate calculation to improve benchmark quality and align with the International Organization of Securities Commissions (IOSCO) Principles for Financial Benchmarks (IOSCO 2013).
• Apply expert judgement when market liquidity falls below pre-defined thresholds where the risk of rate distortion increases substantially.
• Trim the top 5 per cent of transactions to avoid outliers and decrease the level of noise.

Some benchmark rates, such as BBSW and AONIA, have rules guiding how the rates are formed on days when market liquidity is exceptionally low, also known as expert judgement. To identify such a threshold, we analyse the distribution of noise on days with very low liquidity and find that noise tends to be four to five times higher when market activity falls below the following thresholds: total volume between $0.75-$1.5 billion; 10-20 transactions; or 6-14 distinct counterparties.^[1]

This paper contributes to the growing literature on benchmark design, benchmark quality measurement, and the informational content of interest rate benchmarks.

First, we build on theoretical work on optimal benchmark design, particularly in the context of reforms following the LIBOR manipulation scandals. Duffie and Stein (2015) highlight the need for reforms to benchmark construction, arguing that manipulation risks are heightened when benchmarks are based on low transaction volumes or unverifiable submissions. Building on this, Duffie, Dworczak and Zhu (2017) and Duffie and Dworczak (2021) develop formal models of benchmark design that identify trade-offs between robustness, liquidity coverage, and responsiveness to market fundamentals.

Second, this paper contributes to the empirical literature comparing benchmarks. Empirical studies including Schrimpf and Sushko (2019) and Klingler and Syrstad (2021) assess whether alternative reference rates (ARRs) are more robust and less susceptible to manipulation, largely focusing on international benchmarks such as SOFR and SONIA. Indriawan, Jiao and Tse (2021) find that SOFR tracks the Federal Reserve Bank of New York's policy rate more closely than US$ LIBOR, supporting its adoption.

Unlike prior studies that predominantly rely on published daily data, we utilise the transaction-level regulatory dataset to gain deeper insights into the empirical properties of benchmark noise and its determinants. Our method builds on the empirical work of Brugler et al (2025), who evaluate the quality of LIBOR rates and ARRs in the international markets using a state-space model and find that ARRs are less noisy than LIBOR in four of the five markets. We adapt their methodology to a single-rate setting and use the measurable benchmark design features of Beta SOFIA as explanatory variables for noise.

Third, this study relates to the literature on constructing forward-looking term rates from overnight benchmarks. Although we focus on the quality of the overnight rate, our findings are relevant to the construction of term Beta SOFIA or similar derived benchmarks. As Liu and Bai (2022) point out, the absence of a forward-looking component in ARRs presents challenges for rate users. In response, Heitfield and Park (2019) and Skov and Skovmand (2021) propose models based on futures markets to estimate term structures. Our results contribute to this literature by showing that noise in the overnight benchmark may propagate into term rate construction, reinforcing the need to ensure robustness at the overnight tenor.

Methodologically, we apply a state-space model (Brugler et al 2025) to estimate benchmark noise, allowing us to separate transitory deviations from the efficient rate. This approach builds on empirical methods developed in the market microstructure literature to separate price signals from noise. Our approach follows in the spirit of Menkveld, Koopman and Lucas (2007), who estimate information and noise across overlapping trading intervals, and is closely related to Hasbrouck (1999), Figuerola-Ferretti and Gonzalo (2010), and Hendershott and Menkveld (2014). It also draws on literature that uses vector autoregression models to estimate information shares in parallel markets (e.g. Baillie et al 2002; Putniņš 2013; Hasbrouck 2021). More broadly, our work contributes to the literature on price informativeness (e.g. Campbell and Shiller 1988; Morck, Yeung and Yu 2000; Brogaard et al 2022) by offering a real-time metric of how closely a benchmark rate reflects fundamental value.

This paper proceeds as follows. Section 2 discusses the relevant institutional details of interest rate benchmarks and the repo market in Australia and internationally. Section 3 describes our data, including both Beta SOFIA as computed by the ASX and the underlying repo transactions that we analyse. Section 4 presents the state-space model we use to separate signal and noise from different benchmarks, time series and transaction-level regressions that relate noise to different market characteristics, and comparisons of noise across alternative benchmark design choices. Section 5 concludes.

Footnote

In its current design, SOFIA would not be set in the days analysed because they were holidays, see Section 4.4. [1]