The global listed derivatives market has seen considerable growth over the last five years and is expected to continue. A part of the attraction stems from investors being able to speculate on market movements and hedge risk through volatile market conditions since derivatives are bets on the performance of something else, such as stocks, interest rates, or creditworthiness.

According to a 2024 study conducted by Coalition Greenwich, a provider of financial services data, 42% of derivatives investors believe that volatility will drive market growth in 2025. Additional factors that were identified included a surge in retail investor activity and expanded demand coming from India and China.

As the listed derivatives market continues to grow in trade volume and complexity, one of the most significant challenges financial professionals face is the need to navigate an ever-expanding universe of data. This type of data comes from numerous exchange-listed contracts, intraday market activities, and over 160 global exchanges.

Consistent and high-quality metadata in the listed derivatives market is hard to obtain, making data exploration and model building an involved process. Compounding these hurdles is the fact that more exchanges are entering into the listed derivatives market, and existing exchanges are launching more complex products.

Additionally, the securities universe is becoming increasingly volatile with exchanges introducing more zero-day and short-term contracts and new securities being created based on market movement during trading hours.

Streamlined data normalization, identifier management, and derived analytics, which provide insights that aren’t immediately apparent from looking at the raw data, enable financial professionals to focus on building investment strategies, rather than wrangling derivatives data.

Elements of a strong listed derivatives data foundation

1. Data normalization

In the listed derivatives space, gathering a wide variety of content is critical. To paint a picture of the amount of data available to firms today, listed derivatives data spans reference data, pricing data, regulatory data, and analytics for futures and options across single stocks, commodities (agriculture, energy, and metal), indices, and financial instruments (fixed income and currency). Historical data is a necessary input for investors to identify trends and formulate strategies, so this universe of data stretches back for at least 15 years.

At the same time, timely access to this information is vital. Through real-time consolidated market data feeds, investors can access granular, normalized data from each exchange across the global markets. It is crucial that this data is not only quickly delivered but can also be consumed alongside an extensive set of other non-pricing and analytics data, such as earnings releases, corporate events calendars, and more. This is critical for firms to glean a broad market view of investment opportunities.

While it is necessary to gather this much information as quickly as possible, firms must be able to confidently compare and contrast this data across many companies. Data normalization across disparate formats, languages, and standards in which data is sourced ensures this content can work across firms’ front, middle, and back office operations. This type of standardization also is essential for investors to make accurate comparisons regardless of how individual companies report on their performance.

2. Identifier management

One way to standardize derivatives data is to use identifiers like the Financial Instrument Global Identifier (FIGI). FIGIs help firms tie together disparate and fragmented symbologies, eliminate redundant mapping processes, streamline the trade workflow, and reduce operational risk.

FIGIs are unique, non-changing, and perpetual identifiers that link material changes to a given instrument, maintain permanent association with those changes, and provide a consistent historical perspective so nothing gets lost over time. A 12-character alphanumeric and randomly generated ID covering hundreds of millions of active and inactive instruments, the FIGI acts as a Uniform Resource Identifier (URI) to link to a set of metadata that uniquely and clearly describes the instrument.

The characteristics of a FIGI are particularly valuable when it comes to standardizing derivatives data due to the variety and velocity of this type of data. Variety exists in small nuances in strike prices, maturity, and start dates and underlying instruments or commodities. Velocity exists in how many futures and options are created daily, while at the same time, others are maturing or reaching specific dates, and corporate actions may be affecting any number of underlying instruments.

Being able to anchor all these data evolutions to an unchanging unique identifier is critical as the volume of data continues to grow and is now beyond traditional legacy identification methods. While traditional identification uses tag-to-value approaches, FIGI encompasses a native data model and associated metadata, with a defined relationship structure that can be expanded to capture different relationships while maintaining lineage and provenance.

The result: Putting listed derivatives data to work 

Once firms have access to normalized listed derivatives data, this information can be used to elevate a range of use cases, including:

1. Price and liquidity discovery for trading professionals

Trading professionals can streamline trading operations with exchange data and derived analytics, which can also be used by algorithmic trading engines or derivatives pricing engines. Notably, instrument discovery functionality in this data enables investors to quickly navigate through the growing universe of listed derivative securities with automatic updates flagging new trading opportunities.

2. Risk and valuation: Monitoring and rebalancing

To support pricing or risk management workflows, firms can use derivatives data as a primary source or input to calculate implied volatilities for surface generation, or the input for various risk models such as Value at Risk (VaR) and stress testing models/simulations. It also can be used as part of real-time or end-of-day portfolio risk monitoring and rebalancing.

3. Research: Strategy building and back-testing

Investors can also use derivatives data to create complex intraday price-driven investment strategies including arbitrage, trend following and mean reversion. Access to deep point-in-time historical data can be used to uncover market opportunities and back-test strategies more broadly.

4. Operational workflows: Security master and pricing data management

To effectively support the full trade lifecycle for Listed Derivatives, complete, timely, accurate, and high-quality data is table stakes. In particular, comprehensive reference data, including pricing across regions and exchanges is foundational data for investors’ front, middle, and back office systems to aid security identification, creation, trading, and settlement.

So they don’t miss any key developments, investors need to be able to monitor and support tradable listed derivatives prior to market opening and see updates on new instruments listed during the day.

5. Regulatory and compliance

Detailed descriptive data can help investors monitor and identify derivatives subject to evolving regulations including FRTB and MiFID transparency requirements, and ultimately stay ahead of the curve and remain compliant.

Moving ahead with solid listed derivatives data

We are living in the age of data – as the amount of available listed derivatives data only grows, it will become increasingly critical to manage and glean key insights at scale. To accomplish this, firms need a strong underlying technology infrastructure so they can access this data where they need it and ensure it can connect with other datasets such as pricing, regulatory, alternative data, and more. With these components in place, firms will be well-positioned to quickly act on opportunities in the derivatives market.

About the Authors:

Colette Garcia is the Global head of Enterprise Data Real-Time Content at Bloomberg. She is responsible for the Real-Time product suite including BPIPE. These solutions support all asset classes and meet client needs from the front to back office with the highest quality data.

Maureen Gallagher is the Global Head of Enterprise Reference Data at Bloomberg, which spans comprehensive terms & conditions, corporate actions, holdings & ownership and legal entity data. Gallagher is focused on supporting clients globally to use this critical foundational data to feed their operational workflows and inform decision making across the front, middle and back office.

Richard Robinson is Chief Strategist, Open Data and Standards at Bloomberg and has led the Financial Instrument Global Identifier (FIGI) initiative, which is a free and open data standard and unique identifier of financial instruments that can be used across all global asset classes. Robinson authored the book, “Understanding Financial Services Through Linguistics” which explores how to improve the creation and application of data, standards, and regulation in financial services through an applied linguistics lens.