Fixed income investing and risk measurement has been recently receiving a lot of attention. With the increased spotlight, there is also more scrutiny and pressure on underlying risk models to live up to more advanced demands. However, traditional fixed income models are not well placed to satisfy these expectations. Below is a collection of resources that offer more insights and recommendations on this topic.
Leveraging Advanced Statistical Methods to Construct Robust Issuer Credit Curves and Market Surfaces as the Basis for Granular, DTS-style Risk Modeling
Modeling potential losses of a credit-risky bond portfolio based on granular, issuer-level return data is notoriously difficult. A myriad of data-quality concerns arise, driven by a vast, frequently illiquid market for which evaluated pricing is often stale, inconsistent or simply missing. Many issuers have only a small number of bonds outstanding. In fact, generally less than half of the issuers in USD high yield index portfolios have more than one bond outstanding that meets standard requirements for inclusion in a model estimation universe (sufficient maturity, etc.). Thus great care must be used to extract signal from data noise.
Learn more Qontigo's new Granular Fixed-Income Risk Model >
Axioma has developed a new algorithm to produce thousands of robust issuer-specific and rating/sector credit curves. In this blog post, we discuss the challenges associated with credit curve construction, and how we solved them, presented with illustrative interactive charts.
Learn more about credit curve construction >
Fixed income factor investing is getting a lot of attention, with publications on the subject appearing regularly. With the increased spotlight there is also more scrutiny and pressure on underlying risk models to live up to more advanced demands. However, traditional fixed income models are not well placed to satisfy these expectations. So what to do?
Learn more about fixed income factors >
Constructing fixed income factor models has long been an elusive endeavor owing to a number of challenges, not least of which includes cleansing and organizing the underlying fixed income data, or lack thereof. In this blog post, we take a look at how we build credit curves to serve as a foundation for fixed income models and the advantages they have over some other methodologies.
Learn how to build a more responsive model >
Axioma's cloud-native technology used to calculate our enhanced fixed income risk model is powered by Microsoft Azure.