Users of market data include trading, banking, research and financial operations. Accounting and IPV Group. Risk management. Model development and testing. and Regulatory Reporting Group. These different groups require market data that corresponds to different frequencies, recency, granularity, and different time periods. Historically, market data has often been obtained by different groups separately, from different vendors, and as part of other software applications or services. This may require additional reconciliation between the risk team’s daily metrics and the company’s metrics, or create potential regulatory issues if reported data is inconsistent, etc. challenges may arise.
business user
Front-office applications and analytics rely heavily on real-time data such as prices, spreads, and yields to make informed trading decisions. Understanding market trends and dynamics also requires access to broader market information such as volume, open interest, and economic indicators. Execution algorithms and e-trading desks require large amounts of low-latency, real-time and historical market data to support trading, develop and test effective trading strategies, and generate execution quality analysis. Investment banks and research teams require broad access to static and historical company, market, and sector data.
Pricing, Ratings, IPV
Timely, accurate and consistent market data is essential for pricing and valuation. Daily market data is carefully examined alongside trader marks to calculate end-of-day valuations. Product management and finance teams use market data to calculate T+1 P&L and support IPV variance analysis. Importantly, inconsistencies in the data used across these functions can result in frequent reconciliations and pose financial risks to the enterprise. Ensuring consistent and reliable data is essential to maintaining accuracy and mitigating potential risks.
Risk management and stress testing
Extensive historical market data reflecting periods of significant stress is critical to robust risk management and stress testing capabilities. Near real-time market data is required to support intraday risk monitoring activities, while spot market data is required to calculate risk metrics such as sensitivities. Historical market data must be accurate and must allow for continuous historical time series construction and adjustment for risk factor shocks, both in normal operations and for regulatory stress testing.
Model development and validation
Developing and testing pricing, risk management, and capital models relies on consistent and reliable market data across asset classes and products, as well as a common framework to address and remediate data quality challenges. It is necessary. Extensive market data is also required to meet current regulatory requirements. For example, the Fundamental Review of Trading Books (FRTB) is a thorough review of market risk capital requirements. End-of-day market data and historical market data time series are critical components for internal model approach (IMA) development such as Expected Shortfall (ES), Stress ES, Profit and Loss Attribution Test (PLAT), and IMA Data Principles .
Companies that use high-quality historical market data time series for risk and capital models achieve financial efficiency by mitigating the impact of inaccurate risk factor modeling and reducing unnecessary capital requirements. You can also.
Trading, analysis and reporting
Trade reporting and trade execution analysis are subject to increased regulation and scrutiny, including Consolidated Audit Trails (CAT), Dodd-Frank, Markets in Financial Instruments Directive (MiFID II) and European Market Infrastructure Regulation (EMIR). Market data is important. these processes. Reporting requirements require aggregation of data across different products and asset classes. Consistent market data integrated with company reference data and customer transaction data is essential to generate accurate and timely reports and provide value-added customer analytics. By combining market data with other relevant data sets, companies can also enhance their business reporting capabilities and derive valuable insights for their clients.
