Market Data Solution

An innovative solution which unifies traditional reference data (static data, corporate actions, end-of-day pricing) with more time-critical market data (intraday pricing, rates, curves, and derived data sets such as volatility surfaces and correlation matrices) and offers increased data quality, consistency and transparency across the investment process.

The GoldenSource Market Data Solution ( or GSMDS) encompasses the capture, cleansing, validation, storage and distribution of a Golden Copy of historic end of day data, intraday and tick data, curves, spread-curves and derived data across all asset classes and instruments.

Risk management, product control and finance officers need higher data quality, transparency and consistency. The GoldenSource Market Data Solution is unique in offering a proven business solution to address the challenges of offering a 360 degree view of all risk-relevant data in a single data management platform

A key step to greater transparency is a consistent, high quality and open foundation for key data sets such as prices, rates, curves and surfaces. Having this consistent data foundation in place can enable:

• Faster Regulatory Approval - regulators are increasingly focussed on the quality and consistency of the data feeding the risk reporting of a financial institution. Additionally, more stringent scenario management and liquidity risk reporting is putting institutions under even greater pressure to have an open, transparent architecture for data and instrument pricing.
• Greater Auditability - the ease with which changes to data can be traced and audited is an important requirement for many regulators.
• Consistent Risk Consolidation - consolidating risks from different trading divisions is rendered useless if each division is using its own (inconsistent) datasets.
• Easier Historic Re-Runs - the ability to reproduce reference, market and derived data "as at" a point in time for backtesting, scenario and client/regulatory reporting is an ever more important requirement.
• Greater Efficiency - many institutions are employing too much manual effort in reconciling between front, middle and back office systems where many of the issues are simply down to inconsistent data.
• Operational Robustness- having a data management framework that can support complex data easily and quickly can mean less reliance and exposure to ad-hoc and poorly documented tactical systems for storing this kind of data.
• Reduced Risk - operational risk can be reduced if P&L and risk reports are less dependent on data and valuations sourced from front-office spreadsheets.
• Faster Analysis - having the same datasets being used in trader spreadsheets as being used in risk systems means that everyone can share their ideas quickly and easily.