on-demand webinar

Superior prepayment predictions for Agency MBS across all market environments

Kay Giesecke

Founder, Chairman and Chief Scientist, Professor at Stanford University

Accurate predictions of prepayment speeds are critical for security selection and portfolio construction in Agency MBS markets. Infima’s transformative deep learning technologies set new prediction standards, delivering performance boosting edges to MBS market participants including investors and dealers. Our CPRs enable high-confidence investment and trading decisions, even in disruptive market environments.

In this webinar, we give an overview of Infima’s deep learning technologies for prepayment prediction. We then discuss the out-of-sample accuracy of our CPRs for the January 2019—October 2021 period, demonstrating robust performance across a range of market regimes including the 2020 pandemic. Granular analyses by issuer, coupon, story cohort and pool balance are used to validate the superior performance of Infima’s speeds at a granular level. S-curves demonstrate that our CPRs accurately capture the sensitivity of prepayments to changes in interest rates. WALA ramps show that Infima’s speeds accurately capture seasoning effects. Finally, we will show that our CPRs provide highly accurate pool rankings, confirming that pools we predict will outperform are indeed extremely likely to do so.

Watch Now

About the Speaker

Kay Giesecke

Founder, Chairman and Chief Scientist, Professor at Stanford University

Kay Giesecke is the Founder, Chairman and Chief Scientist at Infima. He is also Professor of Management Science & Engineering at Stanford University, the director of the Advanced Financial Technologies Laboratory, and the director of the Mathematical and Computational Finance Program. Kay serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk. He is a member of the Council of the Bachelier Finance Society.

Kay is a financial technologist interested in solving the challenging modeling, statistical, and computational problems arising in fixed-income and credit markets. Together with his students at Stanford, Kay has pioneered the core elements of the deep learning and computational technologies underpinning Infima’s solutions.

Kay’s research has won several awards, including the JP Morgan AI Faculty Research Award (2019) and the Fama/DFA Prize (2011), and has been funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, Swiss Re, American Express, Moody's,and several other organizations.

Kay has advised several financial technology startups and has been a consultant to banks,investment and risk management firms, governmental agencies, and supranational organizations.