Pricing and analytics: fixed income – Quantifi

After more than a decade of low rates, investors are facing more uncertainty, increased volatility and shifting liquidity. In this rapidly changing environment, Quantifi continues to deliver high-performance, transparent and adaptable pricing and risk analytics for fixed income and credit markets.

“Fixed income markets have entered a period of higher rates and elevated volatility compared to the past few years,” says Rohan Douglas, chief executive officer at Quantifi. “That means firms need analytics that are both fast and accurate to react to changing market conditions effectively.”

The analytics platform supports a range of instruments, from government and corporate bonds to structured and derivatives products such as swaps, swaptions and caps/floors. The firm’s cross-asset model library underpins valuations across interest rate, credit, equity and commodity markets, allowing institutions to manage exposure and liquidity risk in a unified environment. This breadth of coverage and consistency of modelling is an increasingly important requirement for banks, asset managers and hedge funds operating under tighter regulatory and operational constraints.

“The challenge for our clients isn’t just about pricing accurately,” Douglas adds. “It’s about managing the entire lifecycle of risk – from valuation and margining to XVA and capital allocation –within a single coherent framework. Our architecture lets them do that without the operational friction that comes from running multiple systems.”

Quantifi’s architecture is designed for speed and scale, drawing on multicore and cloud computing to deliver results in near-real time. This performance proved valuable during the sharp market moves of 2025, when clients relied on the system to value complex portfolios. When yield curves repriced and credit spreads widened, they were able to run fast, accurate valuations alongside stress tests and scenario analysis. The integration of more than 40 price, risk and relative-value metrics for bonds allowed for granular assessments of portfolio sensitivities, convexity and spread behaviour across different curve environments.

The challenge for our clients isn’t just about pricing accurately, it’s about managing the entire lifecycle of risk ... within a single coherent framework. Our architecture lets them do that without the operational friction that comes from running multiple systems

Rohan Douglas, Quantifi

A key strength of the platform lies in its support for modern market conventions such as overnight index swap discounting and alternative reference rate frameworks. These are critical to the accurate valuation of interest rate derivatives and have been fully embedded into Quantifi’s analytics. By combining these capabilities with flexible curve construction and robust calibration tools, the firm enables users to align valuations closely with market conditions and internal pricing assumptions, while maintaining auditability and transparency – qualities that are essential for regulatory compliance and investor confidence.

Quantifi’s commitment to openness and extensibility also played an important role in its recognition. Its analytics can be accessed through multiple application programming interfaces (APIs) —including Python, Java and C# – and integrated easily into existing workflows, allowing firms to embed third-party analytics within proprietary systems and connect them to broader data science initiatives. “There’s huge appetite among clients to combine traditional quantitative models with modern data science,” Douglas explains. “That’s why we’ve invested heavily in making our analytics accessible via Python and cloud-native APIs – it lets quants and data teams experiment quickly and deploy at scale.”

Over the past year, Quantifi has deepened this integration through the development of its Data Science Platform (DSP). The DSP combines the firm’s cross-asset analytics with modern cloud-native technologies and big data tools to support open and flexible risk infrastructures. Users can perform ad hoc portfolio investigations in a sandbox environment, integrate structured and unstructured data, and generate reports through mainstream business intelligence tools. The platform’s scalability and interoperability with Quantifi’s model library create a bridge between quantitative research and live risk management – an area of growing interest as institutions push towards data-driven decision-making.

Automation of lifecycle events – such as defaults, index rolls and versioning – reduces manual intervention in complex credit products, improving operational efficiency. Enhancements to curve calibration, especially for regional markets such as Australia, and expanded coverage for equity financing and exchange-traded fund products further broaden the system’s applicability.

Market participants report that these capabilities translate into tangible benefits: faster time-to-market for new products, reduced operational risk, and greater agility in volatile conditions. The richness of Quantifi’s fixed income and credit models allows clients to fine-tune them to match internal methodologies without sacrificing consistency or validation standards. The company’s quantitative and technical teams play a key role in maintaining this reliability, offering practical support with calibration, portfolio analysis and scenario implementation, and responding quickly to client requests and market developments.

Looking ahead, Quantifi sees continued convergence between model analytics, data science and cloud-based risk infrastructure. “The next phase for us is about scaling these capabilities,” says Douglas. “As clients adopt more data-driven and automated workflows, we’re focused on ensuring our analytics evolve with them – faster, more connected and more transparent across the enterprise.”

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