OTC pricing data explained

OTC pricing data

A key challenge in OTC markets is that observable trading activity is often fragmented and unevenly distributed. Activity tends to concentrate in the most liquid instruments, while many parts of the market trade infrequently.

However, market participants still need complete, consistent data sets to support workflows such as valuation, risk modelling and analytics.

This is why different forms of pricing data exist. They represent different approaches to:

• capturing observable activity
• supplementing gaps
• transforming raw data into usable outputs

To understand these differences, it is helpful to think in terms of a pricing spectrum, which reflects how close a given data set is to underlying market activity.

The OTC pricing data spectrum

The simplest way to understand OTC pricing data is as a spectrum.

At one end is raw market evidence, derived directly from trading activity. At the other is highly processed data, built from aggregated or modelled inputs. Understanding where a data set sits on this spectrum is essential when assessing its suitability for trading, valuation and risk workflows.

This spectrum is not about one type of data being “better” than another. Rather, it reflects different trade-offs between:

• observability vs coverage
• accuracy vs completeness
• raw data vs usability

Understanding where a data set sits on this spectrum is essential when assessing how it should be used.

 Trade & order pricing data

Trade and order pricing data is sourced directly from broker-handled transactions and pre-trade activity, capturing:

• executed trades
• live bids and offers
• observable order flow

Because it reflects actual market activity, evidential data provides the strongest form of pricing evidence. It is commonly used in use cases where independence and auditability are critical, such as:

• independent price verification (IPV)
• valuation control
• regulatory reporting
• market surveillance

At Parameta, trade and order data is sourced directly from TP ICAP’s global broking network, with coverage across capital markets and energy & commodities.

Verifiable pricing data

Verifiable pricing data sits one step further along the spectrum. It addresses a key limitation of raw evidential data: while highly accurate, observable activity can be sparse, particularly across less liquid instruments or tenors.

At Parameta, verifiable pricing helps to bridge this liquidity gap by combining:

• observable trade and order data
• indicative broker pricing
• a defined methodology and rules-based approach

to produce calculated bid, mid and ask prices. With Parameta verifiable data, prices are derived using a transparent methodology, outputs are supported by underlying transactional evidence, and additional statistics provide insight into how the price was formed.

The result is a data set that is:

• more complete than raw data
• anchored in observable activity
• transparent and defensible for audit purposes

A simple way to think about this distinction:

• observable data = what the market did or showed
• verifiable data = an evaluated price built from observable data, indicative inputs and methodology

 Indicative pricing data

Indicative pricing plays a central role in OTC markets, particularly in areas where observable trading activity is limited.In many OTC markets, activity is not continuous. It is often concentrated in the most liquid instruments, maturities or strikes, with large parts of the market trading infrequently or not at all during certain periods.

However, market participants still require complete and consistent pricing coverage across:

• full curves
• volatility surfaces
• instrument sets and tenors

to support workflows such as trading, valuation, risk management and analytics.

Indicative pricing exists to bridge this gap.

Indicative pricing remains one of the most widely used forms of OTC pricing data because it enables market participants to move from fragmented observations to a usable, consistent view of the market.

From sparse activity to usable data

Indicative pricing takes limited observable market inputs and transforms them into decision-ready data sets.

This typically involves:

• selecting relevant market inputs, including broker quotes, trades and order flow
• applying modelling, interpolation and extrapolation techniques
• ensuring consistency across instruments, maturities and structures
• applying data quality controls and validation checks

The result is not simply a collection of prices, but a structured representation of the market, even where direct activity is limited.

A simple way to think about this:

• observable data is sparse and incomplete
• indicative pricing creates completeness and usability

What indicative pricing actually represents

Indicative pricing is sometimes misunderstood as being less reliable than transactional data. In practice, it serves a different purpose.

Rather than attempting to replicate individual trades, indicative pricing provides:

• a dealer-informed view of where the market is
• a consistent pricing framework across instruments
• a continuous view of market conditions, even when trading is intermittent

This is why it is widely used in workflows that require coverage and consistency at scale, such as:

• portfolio valuation and monitoring
• risk modelling and VaR calibration
• scenario analysis and stress testing
• intraday market tracking

What drives the quality of indicative pricing

The quality of indicative pricing is not defined by the output alone, but by how it is constructed.

Key factors include:

• Source data: pricing anchored in real trading activity and broker interaction is more likely to reflect true market conditions
• Methodology: the approach used to select inputs, model gaps and maintain consistency across datasets
• Transparency: the ability to understand and interrogate how prices are formed
• Data quality controls: ongoing validation, cleansing and monitoring processes

These factors determine whether indicative pricing is simply a theoretical approximation, or a robust representation of the market.

The role of indicative pricing in practice

In practice, indicative pricing acts as a core dataset across OTC workflows, rather than a supplementary one.

• Firms rely on it because it provides:
• breadth and depth across asset classes and instruments
• consistency across time series and structures
• availability in real time, intraday and historical formats
• integration into trading, risk and analytics systems

At Parameta, indicative pricing is anchored in broker activity from TP ICAP, reflecting a significant share of global OTC liquidity in certain markets. This helps ensure the pricing reflects real underlying conditions, while still delivering the coverage required for large-scale use.

Indicative data can be delivered as:

• prices and yields
• curves and volatility surfaces
• cash prices and differentials
• real-time, intraday and historical datasets

How indicative pricing fits within the spectrum

Within the broader pricing spectrum:

• evidential data provides direct market observations
• indicative pricing provides complete and usable coverage
• evaluated or verifiable data combines both approaches
• composite and consensus data sit further downstream

Indicative pricing is therefore rarely used in isolation. It typically forms the foundation layer, combined with other data types depending on the use case.

Composite pricing

Composite pricing data is constructed by blending submitted prices from multiple contributors into a single output, typically aggregated and distributed by a vendor. These data sets are designed to provide broad market coverage, but rely on contributor panels and aggregated inputs rather than direct access to underlying trading activity.

As a result:

• visibility into contributors and inputs is often limited
• underlying trades and order depth are not typically observable
• methodology may not be fully transparent

Within the pricing spectrum, composite data represents a more processed view of the market, sitting further from direct price formation.

Consensus pricing

Consensus pricing data represents an average of prices or valuations submitted by market participants. Rather than reflecting observable market activity, it provides a summarised view of where participants believe the market is at a given point in time.

Key characteristics include:

• periodic snapshots, such as monthly or intraday submissions
• reliance on participant marks rather than executed trades
• potential for convergence towards the average over time

For this reason, consensus data is typically used as a reference or validation point, rather than a primary source of pricing.

Within the spectrum, it sits at the furthest end, representing the most processed form of pricing data.

Bringing the spectrum together

Each type of OTC pricing data plays a different role within market workflows.

• trade and order data provides direct, observable evidence of market activity
• verifiable pricing builds on that evidence to create defensible, evaluated outputs
• indicative pricing delivers the complete, consistent coverage required for large-scale use

These categories represent data that is closest to the point of price formation.

Further along the spectrum:

• composite data provides aggregated views based on contributor inputs
• consensus data provides averaged views of participant valuations

Understanding these distinctions is critical when selecting pricing data, as the level of processing directly impacts transparency, observability and suitability for different use cases.