Faced with a deluge of risk, compliance and cost issue, risk professionals can no longer rely on classic analytic techniques for managing fixed income portfolios.

Instead, they are using “derived analytics,” i.e., analytics that combine fixed income analytics with security master data these analytics to manage portfolios.

Thomson Reuters and BlackRock have teamed up to offer a derived analytics service created via BlackRock Solutions’ analytics infrastructure Aladdin, using the interest rate, credit, mortgage and risk models developed internally by BlackRock, and Thomson Reuters DataScope data and evaluated pricing service.

Derived analytics are designed to help institutional asset managers, hedge funds, banks, insurance companies, sovereign wealth funds, corporate treasuries and family offices better validate and manage their fixed income portfolios to mitigate portfolio risk.

Interest rates are at lows not seen in decades and there’s no question that economic growth has been sluggish in recent years — and the result has been a flattened yield curve.

Meanwhile, increased focus on risk has driven investor demand for greater transparency into portfolios, and the regulatory environment has grown far more complex, with firms now hampered by increased requirements for stress testing and macroeconomic scenario analysis.

“Now more than ever, analytics have become a critical tool for managing these challenges for more effective risk management and profitable investing,” said Dennis Kirincich, managing director at BlackRock Solutions, and Jayme Fagas, global head of evaluated pricing at Thomson Reuters, in a white paper. “In this new normal, fixed-income portfolio and risk managers need sophisticated analytics and modeling capabilities to effectively generate returns, mitigate risk and manage expanding regulatory requirements.”

Using derived fixed income analytics provides organizations with the confidence that they have the most accurate view of the securities and instruments they hold, and helps them assess and optimize the risk and performance of their portfolios, they said.

Derived analytics have an important role in portfolio analytics, performance, and risk and compliance. However, obtaining fit-for-purpose analytics can be a daunting task.

“Obtaining fit-for-purpose analytics involves hours of software selection, process and review,” said Kirincich and Fagas. “Access to data and the cost of producing these values can skyrocket.

Overall, there are three key factors tat summarize the complexity of this task: production costs, data costs and quality control.

“Performing valuation and risk analysis on fixed-income instruments involves complicated cash flow modeling and pricing assumptions, especially if the instrument has underlying collateral or embedded options,” said Kirincich and Fagas. “Deep domain expertise is required to build the proper models.”

Robust analytics can’t be produced without an adequate supply of market data, they said. Market data includes dynamic data such as trade prices, broker quotes, performance data yield curves and volatilities. It also includes static data such as bond terms and conditions.

“Full comprehensive data is difficult to source depending upon the asset class and market jurisdiction,” said Kirincich and Fagas. “However, a global effort is needed to source all market data.

It’s also necessary to ensure quality control to ensure that analytics and market data are robust, they said.