RDP 2026-03: Designing an Efficient Reference Rate: Lessons from SOFIA 2. Institutional Background

Interest rate benchmarks are reference rates used to price a vast array of financial products, including corporate loans, bonds, and derivatives such as swaps and futures. In Australia, retail lending products such as mortgages are generally not directly linked to these benchmarks, although changes in benchmark rates affect financial institutions' funding costs and can influence lending rates offered to households and businesses.

An appropriate benchmark reflects actual borrowing costs in the market and enables consistent valuation across financial contracts. These rates affect trillions of dollars in notional value globally.

2.1 Australian interest rate benchmarks

In Australia, there are two main interest rate benchmarks and if fully established Beta SOFIA would become the third:

• BBSW: A credit-sensitive term benchmark used widely in corporate lending and derivatives. As of November 2024, $35 trillion in notional value of futures contracts reference BBSW (ASX 2024). BBSW is administered by the ASX and calculated using a volume-weighted average price (VWAP) methodology based on eligible transactions in prime bank paper. The rate is published for tenors ranging from one to six months, with the 3-month and 6-month tenors accounting for most trading activity. In 2024, the average daily volume in the 3-month tenor was approximately $0.6 billion, and $0.7 billion in the 6-month tenor.^[2] In 2018, the BBSW methodology was enhanced to incorporate a transaction-based layer in its calculation waterfall, aligning it with the IOSCO Principles for Financial Benchmarks and recommendations from the Council of Financial Regulators to strengthen the benchmark's robustness and integrity (ASX nd).
• AONIA: An unsecured, nearly risk-free, overnight rate calculated by the Reserve Bank of Australia (RBA) (Bristow and de Roure 2023). AONIA is calculated as the weighted average of the interest rates at which overnight unsecured funds are transacted in the domestic interbank market. In 2024, the underlying value of AONIA-setting transactions was on average $1.3 billion daily (RBA statistical table F1). AONIA is administered by the RBA and serves as the operational target for monetary policy implementation. Contracts referencing AONIA include Australian dollar overnight indexed swaps (OIS) and the ASX's 30-day interbank cash rate futures contract.
• Beta SOFIA: A newly developed secured overnight rate based on general collateral (GC1) repo transactions conducted by Austraclear participants. The rate is currently published in beta version by the ASX. In 2024, the underlying value of SOFIA-setting transactions was, on average, $7 billion daily. Beta SOFIA calculation uses a volume-weighted trimmed mean approach, which excludes the bottom 25 per cent of volume from the yield distribution.^[3] SOFIA is not currently designated as a financial benchmark and has not been assessed for compliance with IOSCO Principles for Financial Benchmarks, alignment with these standards would support its future development. SOFIA addresses a gap in repo market infrastructure and transparency.

2.2 International interest rate benchmarks

LIBOR used to be the world's dominant reference rate, underpinning contracts worth over US$200 trillion globally. It was calculated each business day based on submissions from a panel of large banks, which were asked to estimate the rate at which they could borrow funds on an unsecured basis in the interbank market.

This submission-based approach carried inherent risks. Panel banks had strong incentives to manipulate the rate: to benefit their derivatives positions or to improve perceptions of their creditworthiness (Duffie and Stein 2015). Between 2012 and 2015, several major global banks were fined billions of US dollars for colluding to manipulate LIBOR.

Following the global financial crisis, regulatory reforms led to a steep decline in unsecured interbank lending, the market that underpinned LIBOR. On some days, 3-month USD LIBOR was determined by as few as three to eight actual transactions, despite referencing contracts worth trillions of US dollars. This disconnect between the rate-setting market (low volume) and the referencing market (high volume) revealed LIBOR's fragility.

As a result, global regulators – led by the Financial Stability Board (FSB), the Bank for International Settlements (BIS), and IOSCO – coordinated a transition to transaction-based overnight benchmarks, known as ARRs. LIBOR was phased out in stages, with most tenors in British pound, euro, Swiss franc and Japanese yen discontinued from 31 December 2021, and the final US dollar settings ceasing on 30 September 2024. Market participants have since transitioned to these ARRs:

• United States – SOFR: based on secured overnight Treasury repo transactions.
• United Kingdom – SONIA: based on unsecured overnight deposits.
• Euro area – Euro Short-term Rate (€STR): based on unsecured wholesale euro lending.
• Switzerland – Swiss Average Rate Overnight (SARON): based on secured interbank repos.
• Japan – Tokyo Overnight Average Rate (TONAR): based on unsecured overnight interbank rates.
• Canada – Canadian Overnight Repo Rate Average (CORRA): based on secured overnight repo transactions.

2.3 Trade-offs in benchmark design

2.3.1 International evidence on benchmark design trade-offs

The design of financial benchmarks necessarily involves a range of trade-offs. Brugler et al (2025) explore these trade-offs in the context of benchmark reform in five jurisdictions – United States, United Kingdom, euro area, Switzerland and Japan – where ARRs have been introduced to replace LIBOR. Their analysis identifies several key design choices:

1. Methodology: survey-based versus trade-based approaches to benchmark construction.
2. Underlying market composition: inclusion of only interbank transactions versus a broader pool including wholesale and non-bank counterparties.
3. Collateralisation: use of secured versus unsecured transactions.
4. Aggregation window: the length of time over which transactions are included in the rate calculation.
5. Statistical stabilisation: application of trimming or filtering techniques to mitigate the influence of outliers.
6. Treatment of small trades: whether small-value transactions are weighted or excluded entirely.
7. Contract-to-market ratio: the relative size of derivative exposures referencing the benchmark versus the volume of underlying transactions, which affects manipulation incentives.
8. Multiplicity of benchmarks: whether to maintain a single reference rate or allow for the coexistence of multiple alternatives.

Empirical evidence suggests that robust benchmarks are generally underpinned by deep and liquid markets, consistent with theoretical underpinnings (Duffie and Dworczak 2021). Brugler et al (2025) find that robust benchmarks tend to share certain features: limited scope for manipulation, the use of statistical trimming to reduce noise, and reduced reliance on thin markets with limited transaction depth. However, they argue that no single configuration is optimal across all jurisdictions – benchmark design must be tailored to local market structure and institutional context.

2.3.2 Benchmark design considerations in Australia

Drawing on international experience, several design dimensions are particularly relevant for Australia: the choice of underlying transaction pool, the role of collateralisation, the implementation of statistical stabilisation techniques, the use of expert judgment under low-volume conditions, and the treatment of related-party transactions.

Underlying transaction pool and multi-rate framework

Australia has adopted a multi-rate framework consisting of BBSW and AONIA, with Beta SOFIA being developed as a secured overnight rate that could serve as an additional reference rate in the future. Each is based on distinct transaction types: term unsecured, overnight unsecured, and overnight secured, respectively. This approach allows different segments of the market to choose benchmarks that align with their specific needs. For example, BBSW remains appropriate for contracts requiring credit sensitivity and longer tenors (e.g. 3-month interest rate futures). AONIA is widely used in OIS and other derivatives where minimal credit risk is preferred, reflecting its basis in unsecured overnight cash market transactions. Beta SOFIA's similarity to SOFR (secured, overnight, minimal credit risk) may appeal to participants engaged in international multi-currency swaps involving the US dollar and Australian dollar. While the availability of multiple benchmarks promotes flexibility, it may also lead to fragmentation of liquidity across the referencing markets.

Secured versus unsecured transactions

Beta SOFIA is based on secured repo transactions, which makes it sensitive to volatility in collateral markets. That is, there are many transactions that are motivated for reasons other than raising cash. In contrast, unsecured rates like AONIA may offer more stability but transaction volume tends to be smaller and the group of participants narrower. To manage volatility in secured benchmarks, statistical stabilisation techniques such as trimming are often employed.

Statistical stabilisation (trimming)

Trimming methods that remove extreme observations from the transaction set have been shown to reduce benchmark noise, as in the case of post-reform SONIA since 2018. This is particularly relevant in the repo market, where some transactions may reflect cash settlements from other markets and result in rates that differ materially from prevailing market conditions. While trimming improves the reliability of the benchmark, it also reduces the number of transactions used in the calculation. This presents a trade-off between lowering noise and maintaining sufficient volume to ensure the rate remains representative and less susceptible to manipulation.

Expert judgement and thin markets

Expert judgement may be applied when market liquidity is insufficient for rate calculation. In such cases, expert judgement can improve market efficiency and reduce manipulation risk by allowing the administrator to exclude anomalous transaction days. Depending on the benchmark design, expert judgement may involve administrator discretion or the application of pre-defined rules. For example, the Federal Reserve Bank of New York and the Bank of Canada have discretion to apply expert judgement in the calculation of SOFR and CORRA, respectively. The RBA also applies a pre-established set of rules when the cash rate crosses pre-defined expert judgement thresholds. Nevertheless, excessive reliance on subjective inputs may reduce transparency and distort the benchmark's reflection of actual market conditions. The trade-off between robustness and transparency must therefore be carefully managed.

Related-party transactions and manipulation risk

The IOSCO Principles for Financial Benchmarks only allow for arms-length transactions to be included in the rate calculation (IOSCO 2013). That is, transactions between legally related parties should be excluded from benchmark rates. This is because the inclusion of related-party transactions in benchmark calculations can create opportunities for manipulation. International benchmarks exclude such trades (see Table A1 for details). Moreover, related-party transactions may not be an accurate description of market condition as rates transacted often reflect banks' internal capital markets, which might diverge from current market conditions. At present, it is impossible to perfectly identify related-party transactions in Beta SOFIA and for this reason they are not excluded. This could raise concerns about benchmark integrity when the benchmark is formally launched. The empirical analysis in this paper examines the extent to which the inclusion of related-party transactions contributes to benchmark noise.

Footnotes

Available at <https://www.asx.com.au/content/dam/asx/benchmarks/asx-interbank-bbsw-daily-volume-report.xlsx>. [2]

Currently, the ASX publishes Beta SOFIA in two forms: volume-weighted trimmed mean and median. Going forward, the ASX is only considering the volume-weighted trimmed mean approach. [3]