The metrics are based on ICE’s established Liquidity Indicators™ service, which provides an independent, near-term view of relative liquidity to support risk management, investment decision making and regulatory obligations. The Liquidity Metrics include:
Time-weighted ADV Active
A forward-looking estimate of daily trading volume based on observed activity within the most recent three months.
Turnover Active
A measure of observed trading activity as a proportion of outstanding value.
Projected Daily Volume Capacity
A projection of potential daily trading volume that combines recent activity with factors such as bond size, time since issuance, or issuer-level observed trading
Turnover Capacity
Projected trade capacity expressed as a ratio of the outstanding amount of a bond.
The new measures allow users to choose from metrics that capture a bond’s recent trading history, or a modelled approach that applies additional inputs to assess the likelihood it can be traded, and in what size. The latter approach is important where a bond may not have traded in the past three months - so has no recent ADV (average daily volume) data - yet may have other characteristics relevant to its liquidity like size, issuer level trading activity, or related quote activity.
With ICE Liquidity Metrics for Fixed Income Indices help users looking for:
- Deeper understanding of liquidity - ICE’s fixed income index Liquidity Metrics provide a deeper, more uniform understanding of liquidity variation across asset segments and market conditions to help researchers better quantify the relationship between bid-offer spreads and volume-based liquidity metrics, as well as help potentially provide ETF issuers and investors with clearer visibility into premium/discount behaviors.
- Risk assessment- in fixed income markets could also be refined through granular liquidity analysis. Both asset managers and issuers could apply these insights to inform index design and methodology, incorporating elements demonstrated to support liquidity.
- Bond market segment comparison - ICE Data Indices' extensive coverage universe and standardized index constituent data - allow users to compare liquidity metrics across diverse bond market segments around the world.
- Speedy analysis - The ICE Custom Index Tool provides a web-based platform for analyzing the liquidity impact of user decisions on custom indices representing alternative investment strategies.
Key use cases
| User | Key Benefits |
|---|---|
| Asset Manager | Test and refine index designs using daily liquidity metrics. Quickly assess how design choices affect overall liquidity. Gain insights to reduce transaction costs and understand market behavior (e.g., liquidity drop-off in seasoned bonds). |
| Market Makers and APs | Use 'capacity' metrics to better price thinly traded bonds and manage ETF creation/redemption. Helps build custom baskets and manage fund liquidity. Supports NAV premium/discount arbitrage. |
| Regulatory requirements | Supports oversight and stress-testing of bond ETFs. Used for compliance with SEC Rule 22e-4 and EU/UK UCITS requirements. Enables historical and hypothetical liquidity simulations. |
| ETF Ecosystem | Links bid-offer spreads with liquidity metrics for clearer premium/discount visibility. Highlights how trade capacity can erode even when spreads remain stable. |
ICE's Liquidity Metrics for fixed income indices incorporate four years of historical data and are accessible through the ICE Index Platform. As data and technology in bond trading continue to evolve, these metrics represent the first phase of ICE's broader efforts to integrate liquidity-based concepts with its industry-leading fixed income indices.
Related insights
Liquidity metrics for bond indices offer rich opportunity
ICE’s Liquidity Metrics could provide a deeper understanding of variation across fixed income segments and be used to inform index design.
Liquidity interview with ETF Stream
Coming soon.
Indexing: the next evolution
Varun Pawar, ICE Fixed Income & Data Services’ Chief Product Officer, spoke with The DESK about the latest trends in funds management, the use of AI, and how index providers are keeping pace with growing demand for customization.